Skip to main content

Improving the Reliability of Electric Power Infrastructure Using Distributed Solar Generation: An Agent-Based Modeling Approach

  • Conference paper
  • First Online:
Proceedings of the Canadian Society of Civil Engineering Annual Conference 2021 (CSCE 2021)

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 251))

Included in the following conference series:

  • 706 Accesses

Abstract

Natural disasters, such as storms, hurricanes, and earthquakes, are major factors that cause disruptions of electrical power services. Improving the reliability of power infrastructure systems against such events is a major goal in research and practice. Distributed Solar Generation (DSG) can improve system reliability against such disruption of services by providing alternative sources of electrical power, located at the end-consumers, and detachable from the conventional grid. However, the growing adoption of DSG creates many challenges and uncertainties for system operators. As such, the goal of this research is to investigate the benefits of DSG in improving the reliability of the electric power infrastructure. To achieve that goal, an Agent-Based Modeling (ABM) framework is introduced to simulate the integration of DSG into the power infrastructure and markets. The model combines an ABM approach with reliability assessment of power infrastructure systems, aimed to determine the DSG resources required to mitigate the effect of natural disasters on the electric power infrastructure. Results of the complex ABM model verify the suitability of the developed framework in improving power system reliability against natural disasters. Ultimately, this research shall benefit researchers and practitioners in the field of power infrastructure systems reliability and DSG.

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 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 279.99
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. Abbey C, Cornforth D, Hatziargyriou N, Hirose K, Kwasinski A, Kyriakides E, Platt G, Reyes L, Suryanarayanan S (2014) Powering through the storm: microgrids operation for more efficient disaster recovery. IEEE Power Energ Mag 12(3):67–76. https://doi.org/10.1109/MPE.2014.2301514

    Article  Google Scholar 

  2. Abraham YS, Anumba CJ, Asadi S (2018) Exploring agent-based modeling approaches for human-centered energy consumption prediction, pp 368–378. https://doi.org/10.1061/9780784481301.037

  3. Ahmed MO, El-adaway IH, Coatney KT, Eid MS (2016) Construction bidding and the winner’s curse: game theory approach. J Constr Eng Manag 142(2):04015076. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001058

    Article  Google Scholar 

  4. Arghandeh R, Brown M, Rosso AD, Ghatikar G, Stewart E, Vojdani A, von Meier A (2014) The local team: leveraging distributed resources to improve resilience. IEEE Power Energ Mag 12(5):76–83. https://doi.org/10.1109/MPE.2014.2331902

    Article  Google Scholar 

  5. ASCE (2017) Infrastructure report card: energy. ASCE

    Google Scholar 

  6. Azar E, Al Ansari H (2017) Multilayer agent-based modeling and social network framework to evaluate energy feedback methods for groups of buildings. J Comput Civ Eng 31(4):04017007. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000651

    Article  Google Scholar 

  7. Batouli M, Mostafavi A (2014) A hybrid simulation framework for integrated management of infrastructure networks. In: Proceedings of the winter simulation conference 2014, pp 3319–3330. https://doi.org/10.1109/WSC.2014.7020166

  8. Burger SP, Luke M (2017) Business models for distributed energy resources: a review and empirical analysis. Energy Policy 109:230–248. https://doi.org/10.1016/j.enpol.2017.07.007

    Article  Google Scholar 

  9. Choi B, Lee S (2018) An empirically based agent-based model of the sociocognitive process of construction workers’ safety behavior. J Constr Eng Manag 144(2):04017102. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001421

    Article  Google Scholar 

  10. Driesen J, Katiraei F (2008) Design for distributed energy resources. IEEE Power Energ Mag 6(3):30–40. https://doi.org/10.1109/MPE.2008.918703

    Article  Google Scholar 

  11. EIA (2015) EIA electricity data now include estimated small-scale solar PV capacity and generation. https://www.eia.gov/todayinenergy/detail.php?id=23972

  12. EIA (2020) Electricity data. https://www.eia.gov/electricity/data.php

  13. Eid C, Codani P, Perez Y, Reneses J, Hakvoort R (2016) Managing electric flexibility from distributed energy resources: a review of incentives for market design. Renew Sustain Energy Rev 64:237–247. https://doi.org/10.1016/j.rser.2016.06.008

    Article  Google Scholar 

  14. Eid MS, El-adaway IH (2017) Integrating the social vulnerability of host communities and the objective functions of associated stakeholders during disaster recovery processes using agent-based modeling. J Comput Civ Eng 31(5):04017030

    Article  Google Scholar 

  15. Eid MS, El-adaway IH (2017) Sustainable disaster recovery: multiagent-based model for integrating environmental vulnerability into decision-making processes of the associated stakeholders. J Urban Plann Dev 143(1):04016022. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000349

    Article  Google Scholar 

  16. El-adaway IH, Sims C, Eid M, Liu Y, Ali GG (2020) Simulating DER adoption in wholesale power markets using agent based computational economics. Constr Res Congr 2020:120–128. https://doi.org/10.1061/9780784482858.014

    Article  Google Scholar 

  17. El-adaway IH, Sims C, Eid MS, Liu Y, Ali GG (2020) Preliminary attempt toward better understanding the impact of distributed energy generation: an agent-based computational economics approach. J Infrastruct Syst 26(1):04020002. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000527

    Article  Google Scholar 

  18. Elsayegh A, Dagli CH, El-adaway IH (2020) An agent-based model to study competitive construction bidding and the winner’s curse. Proc Comput Sci 168:147–153. https://doi.org/10.1016/j.procs.2020.02.278

    Article  Google Scholar 

  19. Executive Office of the President (2013) Economic benefits of increasing electric grid resilience to weather outages. The White House

    Google Scholar 

  20. Goldfarb D, Idnani A (1983) A numerically stable dual method for solving strictly convex quadratic programs. Math Program 27(1):1–33. https://doi.org/10.1007/BF02591962

    Article  MathSciNet  MATH  Google Scholar 

  21. Gupta R, Bruce-Konuah A, Howard A (2019) Achieving energy resilience through smart storage of solar electricity at dwelling and community level. Energy Build 195:1–15. https://doi.org/10.1016/j.enbuild.2019.04.012

    Article  Google Scholar 

  22. Hagberg A, Swart P, Chult SD (2008) Exploring network structure, dynamics, and function using networkx (LA-UR-08-05495; LA-UR-08-5495. Los Alamos National Lab. (LANL), Los Alamos, NM United States. https://www.osti.gov/biblio/960616

  23. Hunter JD (2007) Matplotlib: a 2D graphics environment. Comput Sci Eng 9(3):90

    Article  Google Scholar 

  24. Kluyver T, Ragan-Kelley B, Pérez F, Granger BE, Bussonnier M, Frederic J, Kelley K, Hamrick JB, Grout J, Corlay S (2016) Jupyter Notebooks-a publishing format for reproducible computational workflows, vol 2016

    Google Scholar 

  25. Lam SK, Pitrou A, Seibert S (2015) Numba: A LLVM-based Python JIT compiler. In: Proceedings of the second workshop on the LLVM compiler infrastructure in HPC, pp 1–6. https://doi.org/10.1145/2833157.2833162

  26. Mahmud K, Khan B, Ravishankar J, Ahmadi A, Siano P (2020) An internet of energy framework with distributed energy resources, prosumers and small-scale virtual power plants: an overview. Renew Sustain Energy Rev127:109840. https://doi.org/10.1016/j.rser.2020.109840

  27. McKinney W (2011) Pandas: a foundational Python library for data analysis and statistics. Python High Performance Sci Comput 14(9)

    Google Scholar 

  28. Millman KJ, Aivazis M (2011) Python for scientists and engineers. Comput Sci Eng 13(2):9–12

    Article  Google Scholar 

  29. Najafi Tari A, Sepasian MS, Tourandaz Kenari M (2021) Resilience assessment and improvement of distribution networks against extreme weather events. Int J Electr Power Energy Syst125:106414. https://doi.org/10.1016/j.ijepes.2020.106414

  30. Nosratabadi SM, Hooshmand R-A, Gholipour E (2017) A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems. Renew Sustain Energy Rev 67:341–363. https://doi.org/10.1016/j.rser.2016.09.025

    Article  Google Scholar 

  31. Oliphant TE (2006) A guide to NumPy (Vol. 1). Trelgol Publishing, USA

    Google Scholar 

  32. Oliphant TE (2007) Python for scientific computing. Comput Sci Eng 9(3):10–20

    Article  Google Scholar 

  33. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D (2011) Scikit-learn: machine learning in Python. Mach Learn Python 6

    Google Scholar 

  34. Pudjianto D, Ramsay C, Strbac G (2007) Virtual power plant and system integration of distributed energy resources. IET Renew Power Gener 1(1):10–16. https://doi.org/10.1049/iet-rpg:20060023

    Article  Google Scholar 

  35. Seabold S, Perktold J (2010) Statsmodels: econometric and statistical modeling with python. In: Proceedings of the 9th Python in science conference, vol 57, p 61

    Google Scholar 

  36. Sharpton T, Lawrence T, Hall M (2020) Drivers and barriers to public acceptance of future energy sources and grid expansion in the United States. Renew Sustain Energy Rev126:109826. https://doi.org/10.1016/j.rser.2020.109826

  37. Sheppard K (2017) Linear model estimation—linearmodels 4.5 documentation. Linearmodels. https://bashtage.github.io/linearmodels/doc/index.html

  38. Sun J, Tesfatsion L (2007) Dynamic testing of wholesale power market designs: an open-source agent-based framework. Comput Econ 30(3):291–327. https://doi.org/10.1007/s10614-007-9095-1

    Article  MATH  Google Scholar 

  39. Sun J, Tesfatsion L (2007b) An agent-based computational laboratory for wholesale power market design. In: 2007 IEEE power engineering society general meeting, pp 1–6. https://doi.org/10.1109/PES.2007.385709

  40. Van Der Walt S, Colbert SC, Varoquaux G (2011) The NumPy array: a structure for efficient numerical computation. Comput Sci Eng 13(2):22

    Article  Google Scholar 

  41. Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, Burovski E, Peterson P, Weckesser W, Bright J, van der Walt SJ, Brett M, Wilson J, Millman KJ, Mayorov N, Nelson ARJ, Jones E, Kern R, Larson E, van Mulbregt P (2020) SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods 17(3):261–272. https://doi.org/10.1038/s41592-019-0686-2

  42. Wang Y, Chen C, Wang J, Baldick R (2016) Research on resilience of power systems under natural disasters—a review. IEEE Trans Power Syst 31(2):1604–1613. https://doi.org/10.1109/TPWRS.2015.2429656

    Article  Google Scholar 

  43. Waskom M, Botvinnik O, O’Kane D, Hobson P, Ostblom J, Lukauskas S, Gemperline DC, Augspurger T, Halchenko Y, Cole JB, Warmenhoven J, Ruiter Jde, Pye C, Hoyer S, Vanderplas J, Villalba S, Kunter G, Quintero E, Bachant P, Qalieh A (2018) mwaskom/seaborn: V0.9.0 (July 2018). Zenodo. https://doi.org/10.5281/zenodo.1313201

  44. Yang Z, Nazemi M, Dehghanian P, Barati M (2020) Toward resilient solar-integrated distribution grids: harnessing the mobility of power sources. In: 2020 IEEE/PES transmission and distribution conference and exposition (T D), pp 1–5. https://doi.org/10.1109/TD39804.2020.9299986

  45. Yuan Y, Wu L, Song W, Jiang Z (2009) Collaborative control of microgrid for emergency response and disaster relief. In: 2009 international conference on sustainable power generation and supply, pp 1–5. https://doi.org/10.1109/SUPERGEN.2009.5348229

  46. Zhang S (2016) Innovative business models and financing mechanisms for distributed solar PV (DSPV) deployment in China. Energy Policy 95:458–467. https://doi.org/10.1016/j.enpol.2016.01.022

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to I. H. El-adaway .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 Canadian Society for Civil Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ali, G.G., El-adaway, I.H. (2023). Improving the Reliability of Electric Power Infrastructure Using Distributed Solar Generation: An Agent-Based Modeling Approach. In: Walbridge, S., et al. Proceedings of the Canadian Society of Civil Engineering Annual Conference 2021 . CSCE 2021. Lecture Notes in Civil Engineering, vol 251. Springer, Singapore. https://doi.org/10.1007/978-981-19-1029-6_45

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-1029-6_45

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-1028-9

  • Online ISBN: 978-981-19-1029-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics