Skip to main content

A Review of Mathematical Optimization Applications in Renewable Energy-Powered Microgrids

  • Conference paper
  • First Online:
Advances in Manufacturing Engineering

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

Abstract

Renewable energy sources are being increasingly adopted because there is a need for alternative sources of power generation which are emission-free and environmentally friendly. These sources include solar energy, wind energy, marine energy, geothermal energy and battery storage systems. Mathematical optimization techniques have increasingly been deployed in sizing and scheduling renewable energy sources in microgrids. In this study, a systematic review of various energy sources, mathematical optimization techniques and applications of mathematical optimization techniques in renewable energy-powered microgrid is presented. It is observed from the review that mathematical optimization techniques have been used with great success in this domain. The review concludes with research gaps for future exploration.

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 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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

References

  1. Ellabban O, Abu-Rub H, Blaabjerg F (2014) Renewable energy resources: current status, future prospects and their enabling technology. Renew Sustain Energy Rev 39:748–764

    Article  Google Scholar 

  2. Gielen D, Boshell F, Saygin D, Bazilian MD, Wagner N, Gorini R (2019) The role of renewable energy in the global energy transformation. Energy Strateg Rev 24((January)):38–50

    Article  Google Scholar 

  3. Nwulu NI, Agboola PO (2012) Modelling & predicting electricity consumption using artificial neural networks. In: Proceedings of the 11th international conference on environmental & electrical engineering (EEEIC2012), Venice, Italy, 18–25 May 2012

    Google Scholar 

  4. Nwulu NI, Fahrioglu M (2011) A neural network model for optimal demand management contract design. In: Proceedings of the 10th international conference on environmental & electrical engineering (EEEIC2011), Rome, Italy, 8–11 May 2011

    Google Scholar 

  5. Fahrioglu M, Nwulu NI (2012) Investigating a ranking of loads in avoiding potential power system outages. J Electr Rev (Przeglad Elektrotechniczny) Warsaw, Poland 88(11a):239–242

    Google Scholar 

  6. Wang J, Du P, Niu T, Yang W (2017) A novel hybrid system based on a new proposed algorithm—Multi-Objective Whale Optimization Algorithm for wind speed forecasting. Appl Energy 208((September)):344–360

    Article  Google Scholar 

  7. Gärttner J, Flath CM, Weinhardt C (2018) Portfolio and contract design for demand response resources. Eur J Oper Res 266:340–353

    Google Scholar 

  8. Batteries Can Help Renewables Reach Full Potential in Africa. [Online]. Available: https://www.worldbank.org/en/news/feature/2019/02/28/batteries-can-help-renewables-reach-full-potential-in-africa. Accessed: 09 May 2019

  9. Jani V, Abdi H (2018) Optimal allocation of energy storage systems considering wind power uncertainty. J. Energy Storage 20((September)):244–253

    Article  Google Scholar 

  10. Hales D (2018) Renewables 2018 global status report

    Google Scholar 

  11. Shadravan S, Naji HR, Bardsiri VK (2019) Engineering applications of artificial intelligence the sailfish optimizer : a novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Engineering Applications of Artificial Intelligence, vol 80, July 2018, pp 20–34

    Google Scholar 

  12. Schmidt M, Schöbel A, Thom L (2019) Min-ordering and max-ordering scalarization methods for multi-objective robust optimization. Eur J Oper Res 275(2):446–459

    Article  MathSciNet  Google Scholar 

  13. Zapotecas-martínez S, García-nájera A, López-jaimes A (2019) Multi-objective grey wolf optimizer based on decomposition. Expert Syst Appl 120:357–371

    Google Scholar 

  14. Gbadamosi S, Nwulu NI, Sun Y (2018) Multi-objective optimization for composite generation and transmission expansion planning considering offshore wind power and feed in tariffs. IET Renew Power Gener 12(14):1687–1697

    Google Scholar 

  15. Damisa U, Nwulu NI, Sun Y (2018) Microgrid energy and reserve management incorporating prosumer behind the-meter resources. IET Renew Power Gener 12(8):910–919

    Article  Google Scholar 

  16. Pintariˇ ZN, Kravanja Z (2015) The importance of proper economic criteria and process modeling for single- and multi-objective optimizations. Comput Chem Eng 83:35–47

    Google Scholar 

  17. Twaha S, Ramli MAM (2018) A review of optimization approaches for hybrid distributed energy generation systems: off-grid and grid-connected systems. Sustain Cities Soc 41(May):320–331

    Article  Google Scholar 

  18. Ooka R, Ikeda S (2015) A review on optimization techniques for active thermal energy storage control. Energy Build 106:225–233

    Article  Google Scholar 

  19. Kheiri F (2018) A review on optimization methods applied in energy-efficient building geometry and envelope design. Renew Sustain Energy Rev 92(May 2017):897–920

    Google Scholar 

  20. Bahlawan H, Morini M, Pinelli M, Ruggero P (2019) Dynamic programming based methodology for the optimization of the sizing and operation of hybrid energy plants. Appl Therm Eng 160(December 2018):113967

    Google Scholar 

  21. Guastaroba G, Savelsbergh M, Speranza MG (2017) Adaptive kernel search: a heuristic for solving mixed integer linear programs. Eur J Oper Res 263(3):789–804

    Article  MathSciNet  Google Scholar 

  22. Ogbe E, Li X (2018) Extended cross decomposition for mixed-integer linear programs with strong and weak linking constraints. Comput Chem Eng 119:237–257

    Article  Google Scholar 

  23. Arcuri P, Beraldi P, Florio G, Fragiacomo P (2015) Optimal design of a small size trigeneration plant in civil users: a MINLP (Mixed Integer Non Linear Programming Model). Energy 80:628–641

    Article  Google Scholar 

  24. Vinel A, Krokhmal PA (2017) Discrete optimization mixed integer programming with a class of nonlinear convex constraints. Discret Optim 24:66–86

    Article  Google Scholar 

  25. Seliverstov EY, Karpenko AP (2019) ScienceDirect hierarchical model of parallel metaheuristic optimization hierarchical model of parallel metaheuristic optimization algorithms. Procedia Comput Sci 150:441–449

    Article  Google Scholar 

  26. Han F, Jiang J, Ling Q, Su B (2019) Neurocomputing a survey on metaheuristic optimization for random single-hidden layer feedforward neural network. Neurocomputing 335:261–273

    Article  Google Scholar 

  27. Du W, Zhang M, Ying W, Perc M, Tang K (2018) The networked evolutionary algorithm: a network science perspective. Appl Math Comput 338:33–43

    Article  MathSciNet  Google Scholar 

  28. Salza P, Ferrucci F (2019) Speed up genetic algorithms in the cloud using software containers. Futur Gener Comput Syst 92:276–289

    Article  Google Scholar 

  29. Diego-mas JA, Garzon-leal D, Poveda-bautista R, Alcaide-marzal J (2019) User-interfaces layout optimization using eye-tracking, mouse movements and genetic algorithms. Appl Ergon 78(March):197–209

    Article  Google Scholar 

  30. Zhi H, Liu S (2019) Face recognition based on genetic algorithm. J Vis Commun Image Represent 58:495–502

    Google Scholar 

  31. Yapici H, Cetinkaya N (2019) A new meta-heuristic optimizer: Pathfinder algorithm. Appl Soft Comput J 78:545–568

    Article  Google Scholar 

  32. Abadlia H, Abadlia H, Smairi N, Ghedira K (2018) ScienceDirect a hybrid Immigrants schema for particle swarm optimization algorithm a hybrid immigrants schema for particle swarm optimization. Procedia Comput Sci 126:105–115

    Article  Google Scholar 

  33. Sedlaczek K, Eberhard P (2006) Using augmented Lagrangian particle swarm optimization for constrained problems in engineering. Struct Multidisc Optim 277–286

    Google Scholar 

  34. Li X, Wang H, Li G (2018) Reanalysis assisted metaheuristic optimization for free vibration problems of composite laminates. Compos Struct 206(July):380–391

    Article  Google Scholar 

  35. Bartolucci L, Cordiner S, Mulone V, Rossi JL (2019) Electrical power and energy systems hybrid renewable energy systems for household ancillary services. Electr Power Energy Syst 107(August 2018):282–297

    Google Scholar 

  36. Olorunfemi TR, Nwulu N (2018) Optimization applications in distributed energy resources : review and limitations. In: 2018 international conference on computational techniques, electronics and mechanical systems, pp 446–450

    Google Scholar 

Download references

Acknowledgements

The first author would like to thank the University Research Committee (URC) at the University of Johannesburg for the Study Scholarship Award.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tope Roseline Olorunfemi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Olorunfemi, T.R., Nwulu, N. (2020). A Review of Mathematical Optimization Applications in Renewable Energy-Powered Microgrids. In: Emamian, S.S., Awang, M., Yusof, F. (eds) Advances in Manufacturing Engineering. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-5753-8_55

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-5753-8_55

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-5752-1

  • Online ISBN: 978-981-15-5753-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics