Skip to main content

Microgrid Planning and Modeling

  • Chapter
  • First Online:
Microgrid Architectures, Control and Protection Methods

Part of the book series: Power Systems ((POWSYS))

Abstract

Due to a number of financial and operational difficulties that have lately been faced by power plants, the electricity industry is exploring a concept as the smart grid to address the problems in the future. There will be significant differences in the conventional power system in the transmission into a smart network, such that when the demand increases, the system does not necessarily generate more electricity to meet consumption needs. In other words, power generation will not be directly dependent on consumption; instead, it will function through reducing losses, managing user demand and cooperating with consumers in order to optimize the load. All of the proposed approaches ensure that the balance between generation and consumption is increased without creating inevitable generation. The smart grid is capable of improving the operation of its components via reducing power costs, reducing additional charges, ensuring maintenance and saving costs of electricity generation, meeting demand and helping to protect the environment. Smart grid energy systems have been developing constantly in order to be able to integrate renewable energy resources, energy storage systems, diesel generators, loads, control systems, etc., which are called microgrids or hybrid power systems, where energy management and planning are of critical importance. There are several functions that are to be considered when dealing with the planning of microgrids, such as load forecasting, the uncertainty of renewable sources, reduction of CO2 emissions, etc.

The original version of this chapter was revised: Abstract has been updated. The correction to this chapter can be found at https://doi.org/10.1007/978-3-030-23723-3_32

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover 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

Change history

  • 13 September 2019

    The original version of this book was published with an older version of the abstract in Chapter 2. This has now been corrected and updated.

References

  1. H. Farhangi, Smart Microgrids—Lessons from Campus Microgrid Design and Implementation (CRC Press, New York, 2017)

    Google Scholar 

  2. H. Bevrani, B. Francois, T. Ise, Microgrid Dynamics and Control (Wiley, USA, 2017)

    Book  Google Scholar 

  3. M. Lydia, S.S. Kumar, A.I. Selvakumar, G.E. Prem Kumar, A comprehensive review on wind turbine power curve modeling techniques. Renew. Sustain. Energy Rev. 30, 452–460 (2014)

    Article  Google Scholar 

  4. A. Kaabeche, M. Belhamel, R. Ibtiouen, Sizing optimization of grid-independent hybrid photovoltaic/wind power generation system. Energy 36, 1214–1222 (2011)

    Article  Google Scholar 

  5. G. Tina, S. Gagliano, S. Raiti, Hybrid solar/wind power system probabilistic modelling for long term performance assessment. Solar Energy 80, 578–588 (2006)

    Article  Google Scholar 

  6. R. Belfkira, L. Zhang, G. Barakat, Optimal sizing study of hybrid wind-PV-diesel power generation unit. Solar Energy 85, 100–110 (2011)

    Article  Google Scholar 

  7. A. Abdollahi, M.P. Moghaddam, Investigation of economic and environmental-driven demand response measures incorporating UC. IEEE Trans. Smart Grid 3, 12–25 (2012)

    Article  Google Scholar 

  8. O. Grothe, J. Schnieders, Spatial dependence in wind and optimal wind power allocation—a copula based analysis. Energy Policy 39, 4742–4754 (2011)

    Article  Google Scholar 

  9. G. Tina, S. Gagliano, V.A. Doria, Probability analysis of weather data for energy assessment of hybrid solar-wind power system, in 4th IASME/WSEAS International Conference on Energy, Environment, Ecosystems and Sustainable Development (2008), pp. 217–223

    Google Scholar 

  10. M. Jones, Kumaraswamy’s distribution a beta-type distribution with some tractability. Stat. Methodol. 6, 70–81 (2009)

    Article  MathSciNet  Google Scholar 

  11. P. Kumaraswamy, A generalized probability density function for double-bounded random processes. J. Hydrol. 46, 79–88 (1980)

    Article  Google Scholar 

  12. E.C. Brechmann, U. Schepsmeier, Modeling dependence with C- and D-vine copulas the R package CDVine. J. Stat. Softw. 52(3) (2013)

    Google Scholar 

  13. A. Sklar, Distribution Functions of N Dimensions and Margins, vol. 8 (Publications of the Institute of Statistics of the University of Paris, 1959), pp. 229–231

    Google Scholar 

  14. R.B. Nelsen, An Introduction to Copulas (Springer, 2006)

    Google Scholar 

  15. A. Seifi, K. Ponnambalam, J. Vlach, A unified approach to statistical design centering of integrated circuits with correlated parameters. IEEE Trans. Circuits Syst. Fundam. Theory Appl. 46, 190–196 (1999)

    Article  Google Scholar 

  16. A.E. Gelfand, A.F.M. Smith, Sampling-based approaches to calculating marginal densities. J. Am. Stat. Assoc. 85, 398–409 (1990)

    Article  MathSciNet  Google Scholar 

  17. H. Joe, Multivariate Models and Multivariate Dependence Concepts (Springer-Science and Business Media, B.V., 1997)

    Book  Google Scholar 

  18. S. Demarta, A.J. McNeil, The t Copula and Related Copulas (Department of Mathematics, Federal Institute of Technology, Zurich, 2004)

    MATH  Google Scholar 

  19. A. Saif, K.G. Elrab, H.H. Zeineldin, S. Kennedy, J.L. Kirtley, Multi-objective capacity planning of a PV-wind-diesel-battery hybrid power system, in IEEE International Energy Conference (2010)

    Google Scholar 

  20. Z. Li, C. Zang, P. Zeng, H. Yu, H. Li, Two-stage stochastic programming based model predictive control strategy for microgrid energy management under uncertainties, in IEEE—PMAPS (2016)

    Google Scholar 

  21. H. Markowitz, Portfolio selection. J. Financ. 7, 77–91 (1952)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ali Jafari Aghbolaghi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Aghbolaghi, A.J., Tabatabaei, N.M., Azad, M.K., Tarantash, M., Boushehri, N.S. (2020). Microgrid Planning and Modeling. In: Mahdavi Tabatabaei, N., Kabalci, E., Bizon, N. (eds) Microgrid Architectures, Control and Protection Methods. Power Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-23723-3_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-23723-3_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23722-6

  • Online ISBN: 978-3-030-23723-3

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics