Skip to main content

Risk-Based Performance of Multi-carrier Energy Systems: Robust Optimization Framework

  • Chapter
  • First Online:
Robust Optimal Planning and Operation of Electrical Energy Systems

Abstract

Energy systems may be exposed to various uncertainties. In this chapter, in order to deal with severe uncertainty of upstream network price, robust optimization framework is presented to investigate uncertainty-based operation of multi-carrier energy system. Robust optimization technique determines the worst condition within the uncertainty and prepares appropriate strategies to handle such conditions in a way that safe operation of multi-carrier energy system is warrantied. So, a grid-connected multi-carrier energy system containing renewable and nonrenewable local generation units, combined heat and power (CHP), and boiler as well as electrical and thermal storage systems is studied under experiencing uncertainty of upstream network price, and the results declaring effectiveness of proposed technique are presented for comparison. It should be noted that simulations are carried out under general algebraic modeling system (GAMS) software.

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

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
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. Nojavan, S., Majidi, M., & Zare, K. (2018). Optimal scheduling of heating and power hubs under economic and environment issues in the presence of peak load management. Energy Conversion and Management, 156, 34–44.

    Article  Google Scholar 

  2. Nazari-Heris, M., Abapour, S., & Mohammadi-Ivatloo, B. (2017). Optimal economic dispatch of FC-CHP based heat and power micro-grids. Applied Thermal Engineering, 114, 756–769.

    Article  Google Scholar 

  3. Nazari-Heris, M., Mohammadi-Ivatloo, B., & Gharehpetian, G. (2017). A comprehensive review of heuristic optimization algorithms for optimal combined heat and power dispatch from economic and environmental perspectives. Renewable and Sustainable Energy Reviews, 81, 2128–2143.

    Article  Google Scholar 

  4. Nazari-Heris, M., Mohammadi-Ivatloo, B., Gharehpetian, G. B., & Shahidehpour, M. (2018). Robust short-term scheduling of integrated heat and power microgrids. IEEE Systems Journal, (99), 1–9. https://doi.org/10.1109/JSYST.2018.2837224 (early access)

  5. Majidi, M., Nojavan, S., Esfetanaj, N. N., Najafi-Ghalelou, A., & Zare, K. (2017). A multi-objective model for optimal operation of a battery/PV/fuel cell/grid hybrid energy system using weighted sum technique and fuzzy satisfying approach considering responsible load management. Solar Energy, 144, 79–89.

    Article  Google Scholar 

  6. Haghrah, A., Nazari-Heris, M., & Mohammadi-Ivatloo, B. (2016). Solving combined heat and power economic dispatch problem using real coded genetic algorithm with improved Mühlenbein mutation. Applied Thermal Engineering, 99, 465–475.

    Article  Google Scholar 

  7. Majidi, M., Nojavan, S., & Zare, K. (2017). A cost-emission framework for hub energy system under demand response program. Energy, 134, 157–166.

    Article  Google Scholar 

  8. Nojavan, S., Majidi, M., Najafi-Ghalelou, A., & Zare, K. (2018). Supply side management in renewable energy hubs. In Operation, planning, and analysis of energy storage systems in smart energy hubs (pp. 163–187). Cham: Springer.

    Chapter  Google Scholar 

  9. Majidi, M., Nojavan, S., & Zare, K. (2018). Multi-objective optimization framework for electricity and natural gas energy hubs under hydrogen storage system and demand response program. In Operation, planning, and analysis of energy storage systems in smart energy hubs (pp. 425–446). Cham: Springer.

    Chapter  Google Scholar 

  10. Nojavan, S., Majidi, M., & Esfetanaj, N. N. (2017). An efficient cost-reliability optimization model for optimal siting and sizing of energy storage system in a microgrid in the presence of responsible load management. Energy, 139, 89–97.

    Article  Google Scholar 

  11. Majidi, M., & Nojavan, S. (2017). Optimal sizing of energy storage system in a renewable-based microgrid under flexible demand side management considering reliability and uncertainties. Journal of Operation and Automation in Power Engineering, 5(2), 205–214.

    Google Scholar 

  12. Nojavan, S., Majidi, M., & Zare, K. (2017). Stochastic multi-objective model for optimal sizing of energy storage system in a microgrid under demand response program considering reliability: A weighted sum method and fuzzy satisfying approach. Journal of Energy Management and Technology, 1(1), 61–70.

    Google Scholar 

  13. Majidi, M., Nojavan, S., & Zare, K. (2017). Optimal stochastic short-term thermal and electrical operation of fuel cell/photovoltaic/battery/grid hybrid energy system in the presence of demand response program. Energy Conversion and Management, 144, 132–142.

    Article  Google Scholar 

  14. Nazari-Heris, M., Mehdinejad, M., Mohammadi-Ivatloo, B., & Babamalek-Gharehpetian, G. (2017). Combined heat and power economic dispatch problem solution by implementation of whale optimization method. Neural Computing and Applications, 1–16. https://doi.org/10.1007/s00521-017-3074-9.

  15. Nojavan, S., Majidi, M., & Zare, K. (2017). Risk-based optimal performance of a PV/fuel cell/battery/grid hybrid energy system using information gap decision theory in the presence of demand response program. International Journal of Hydrogen Energy, 42(16), 11857–11867.

    Article  Google Scholar 

  16. Kamyab, F., & Bahrami, S. (2016). Efficient operation of energy hubs in time-of-use and dynamic pricing electricity markets. Energy, 106, 343–355. https://doi.org/10.1016/j.energy.2016.03.074.

    Article  Google Scholar 

  17. Skarvelis-Kazakos, S., Papadopoulos, P., Grau Unda, I., Gorman, T., Belaidi, A., & Zigan, S. (2016). Multiple energy carrier optimisation with intelligent agents. Applied Energy, 167, 323–335. https://doi.org/10.1016/j.apenergy.2015.10.130.

    Article  Google Scholar 

  18. Beigvand, S. D., Abdi, H., & La Scala, M. (2017). A general model for energy hub economic dispatch. Applied Energy, 190, 1090–1111. https://doi.org/10.1016/j.apenergy.2016.12.126.

    Article  Google Scholar 

  19. AlRafea, K., Fowler, M., Elkamel, A., & Hajimiragha, A. (2016). Integration of renewable energy sources into combined cycle power plants through electrolysis generated hydrogen in a new designed energy hub. International Journal of Hydrogen Energy, 41(38), 16718–16728. https://doi.org/10.1016/j.ijhydene.2016.06.256.

    Article  Google Scholar 

  20. Evins, R., Orehounig, K., Dorer, V., & Carmeliet, J. (2014). New formulations of the ‘energy hub’ model to address operational constraints. Energy, 73, 387–398. https://doi.org/10.1016/j.energy.2014.06.029.

    Article  Google Scholar 

  21. Sheikhi, A., Bahrami, S., & Ranjbar, A. M. (2015). An autonomous demand response program for electricity and natural gas networks in smart energy hubs. Energy, 89, 490–499. https://doi.org/10.1016/j.energy.2015.05.109.

    Article  Google Scholar 

  22. Moghaddam, I. G., Saniei, M., & Mashhour, E. (2016). A comprehensive model for self-scheduling an energy hub to supply cooling, heating and electrical demands of a building. Energy, 94, 157–170. https://doi.org/10.1016/j.energy.2015.10.137.

    Article  Google Scholar 

  23. Shariatkhah, M.-H., Haghifam, M.-R., Chicco, G., & Parsa-Moghaddam, M. (2016). Adequacy modeling and evaluation of multi-carrier energy systems to supply energy services from different infrastructures. Energy, 109, 1095–1106. https://doi.org/10.1016/j.energy.2016.04.116.

    Article  Google Scholar 

  24. Rastegar, M., & Fotuhi-Firuzabad, M. (2015). Load management in a residential energy hub with renewable distributed energy resources. Energy and Buildings, 107, 234–242. https://doi.org/10.1016/j.enbuild.2015.07.028.

    Article  Google Scholar 

  25. Rastegar, M., Fotuhi-Firuzabad, M., & Lehtonen, M. (2015). Home load management in a residential energy hub. Electric Power Systems Research, 119, 322–328. https://doi.org/10.1016/j.epsr.2014.10.011.

    Article  Google Scholar 

  26. Sepponen, M., & Heimonen, I. (2016). Business concepts for districts’ energy hub systems with maximised share of renewable energy. Energy and Buildings, 124, 273–280. https://doi.org/10.1016/j.enbuild.2015.07.066.

    Article  Google Scholar 

  27. Xu, X., Jia, H., Wang, D., Yu, D. C., & Chiang, H.-D. (2015). Hierarchical energy management system for multi-source multi-product microgrids. Renewable Energy, 78, 621–630. https://doi.org/10.1016/j.renene.2015.01.039.

    Article  Google Scholar 

  28. Shabanpour-Haghighi, A., & Seifi, A. R. (2016). Effects of district heating networks on optimal energy flow of multi-carrier systems. Renewable and Sustainable Energy Reviews, 59, 379–387. https://doi.org/10.1016/j.rser.2015.12.349.

    Article  Google Scholar 

  29. Orehounig, K., Evins, R., & Dorer, V. (2015). Integration of decentralized energy systems in neighbourhoods using the energy hub approach. Applied Energy, 154, 277–289. https://doi.org/10.1016/j.apenergy.2015.04.114.

    Article  Google Scholar 

  30. Ma, T., Wu, J., & Hao, L. (2017). Energy flow modeling and optimal operation analysis of the micro energy grid based on energy hub. Energy Conversion and Management, 133, 292–306. https://doi.org/10.1016/j.enconman.2016.12.011.

    Article  Google Scholar 

  31. Brahman, F., Honarmand, M., & Jadid, S. (2015). Optimal electrical and thermal energy management of a residential energy hub, integrating demand response and energy storage system. Energy and Buildings, 90, 65–75. https://doi.org/10.1016/j.enbuild.2014.12.039.

    Article  Google Scholar 

  32. Derafshi Beigvand, S., Abdi, H., & La Scala, M. (2016). Optimal operation of multicarrier energy systems using time varying acceleration coefficient gravitational search algorithm. Energy, 114, 253–265. https://doi.org/10.1016/j.energy.2016.07.155.

    Article  Google Scholar 

  33. Wasilewski, J. (2015). Integrated modeling of microgrid for steady-state analysis using modified concept of multi-carrier energy hub. International Journal of Electrical Power & Energy Systems, 73, 891–898. https://doi.org/10.1016/j.ijepes.2015.06.022.

    Article  Google Scholar 

  34. Orehounig, K., Evins, R., Dorer, V., & Carmeliet, J. (2014). Assessment of renewable energy integration for a village using the energy hub concept. Energy Procedia, 57, 940–949. https://doi.org/10.1016/j.egypro.2014.10.076.

    Article  Google Scholar 

  35. Najafi, A., Falaghi, H., Contreras, J., & Ramezani, M. (2016). Medium-term energy hub management subject to electricity price and wind uncertainty. Applied Energy, 168, 418–433. https://doi.org/10.1016/j.apenergy.2016.01.074.

    Article  Google Scholar 

  36. Pazouki, S., & Haghifam, M.-R. (2016). Optimal planning and scheduling of energy hub in presence of wind, storage and demand response under uncertainty. International Journal of Electrical Power & Energy Systems, 80, 219–239. https://doi.org/10.1016/j.ijepes.2016.01.044.

    Article  Google Scholar 

  37. Pazouki, S., Haghifam, M.-R., & Moser, A. (2014). Uncertainty modeling in optimal operation of energy hub in presence of wind, storage and demand response. International Journal of Electrical Power & Energy Systems, 61, 335–345.

    Article  Google Scholar 

  38. Vahid-Pakdel, M., Nojavan, S., Mohammadi-ivatloo, B., & Zare, K. (2017). Stochastic optimization of energy hub operation with consideration of thermal energy market and demand response. Energy Conversion and Management, 145, 117–128.

    Article  Google Scholar 

  39. Sanjari, M., Karami, H., & Gooi, H. (2016). Micro-generation dispatch in a smart residential multi-carrier energy system considering demand forecast error. Energy Conversion and Management, 120, 90–99.

    Article  Google Scholar 

  40. Shariatkhah, M.-H., Haghifam, M.-R., Parsa-Moghaddam, M., & Siano, P. (2015). Modeling the reliability of multi-carrier energy systems considering dynamic behavior of thermal loads. Energy and Buildings, 103, 375–383. https://doi.org/10.1016/j.enbuild.2015.06.001.

    Article  Google Scholar 

  41. Koeppel, G., & Andersson, G. (2009). Reliability modeling of multi-carrier energy systems. Energy, 34(3), 235–244. https://doi.org/10.1016/j.energy.2008.04.012.

    Article  Google Scholar 

  42. Perera, A. T. D., Nik, V. M., Mauree, D., & Scartezzini, J.-L. (2017). Electrical hubs: An effective way to integrate non-dispatchable renewable energy sources with minimum impact to the grid. Applied Energy, 190, 232–248. https://doi.org/10.1016/j.apenergy.2016.12.127.

    Article  Google Scholar 

  43. Shabanpour-Haghighi, A., & Seifi, A. R. (2015). Multi-objective operation management of a multi-carrier energy system. Energy, 88, 430–442. https://doi.org/10.1016/j.energy.2015.05.063.

    Article  Google Scholar 

  44. Maroufmashat, A., Elkamel, A., Fowler, M., Sattari, S., Roshandel, R., Hajimiragha, A., Walker, S., & Entchev, E. (2015). Modeling and optimization of a network of energy hubs to improve economic and emission considerations. Energy, 93, 2546–2558. https://doi.org/10.1016/j.energy.2015.10.079.

    Article  Google Scholar 

  45. La Scala, M., Vaccaro, A., & Zobaa, A. F. (2014). A goal programming methodology for multiobjective optimization of distributed energy hubs operation. Applied Thermal Engineering, 71(2), 658–666. https://doi.org/10.1016/j.applthermaleng.2013.10.031.

    Article  Google Scholar 

  46. Mancarella, P. (2014). MES (multi-energy systems): An overview of concepts and evaluation models. Energy, 65, 1–17. https://doi.org/10.1016/j.energy.2013.10.041.

    Article  Google Scholar 

  47. Nojavan, S., Najafi-Ghalelou, A., Majidi, M., & Zare, K. (2018). Optimal bidding and offering strategies of merchant compressed air energy storage in deregulated electricity market using robust optimization approach. Energy, 142, 250–257.

    Article  Google Scholar 

  48. Nazari-Heris, M., Madadi, S., & Mohammadi-Ivatloo, B. (2018). Optimal management of hydrothermal-based micro-grids employing robust optimization method. In Classical and recent aspects of power system optimization (pp. 407–420). Elsevier.

    Google Scholar 

  49. Nazari-Heris, M., & Mohammadi-Ivatloo, B. (2018). Application of robust optimization method to power system problems. In Classical and recent aspects of power system optimization (pp. 19–32). Elsevier.

    Google Scholar 

  50. Bertsimas, D., & Sim, M. (2003). Robust discrete optimization and network flows. Mathematical Programming, 98(1–3), 49–71.

    Article  MathSciNet  Google Scholar 

  51. The GAMS Software Website. (2017). [Online]. Available: http://www.gams.com/help/index.jsp?topic=%2Fgams.doc%2Fsolvers%2Findex.html

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Majid Majidi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Majidi, M., Nojavan, S., Zare, K. (2019). Risk-Based Performance of Multi-carrier Energy Systems: Robust Optimization Framework. In: Mohammadi-ivatloo, B., Nazari-Heris, M. (eds) Robust Optimal Planning and Operation of Electrical Energy Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-04296-7_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-04296-7_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04295-0

  • Online ISBN: 978-3-030-04296-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics