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Optimization Framework Based on Information Gap Decision Theory for Optimal Operation of Multi-energy Systems

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Robust Optimal Planning and Operation of Electrical Energy Systems

Abstract

Uncertainty has been always one of the challenging issues in power systems. To handle uncertainty, different solutions have been presented such as forecasting technologies. Although forecasting methods have been used to predict parameters with uncertain behavior, forecasts may not be always true; therefore uncertainty modeling is necessary. Uncertainty and its relevant impacts can be analyzed within various energy systems like hub energy systems or so-called multi-energy systems. Hub energy systems containing renewable energy resources should be scheduled to have safe operation under uncertainties of different parameters. In this chapter, by using information gap decision theory (IGDT), a risk-based optimization framework is proposed for optimal operation of hub energy system with considering net price uncertainty. IGDT benefits from two immunity functions determine appropriate operational strategies for robust and optimistic operation of hub energy system against uncertain behavior of net price: robustness and opportunity functions. A mixed-integer nonlinear programming is employed to model robustness and opportunity functions of IGDT. Also, a sample grid-connected hub system is analyzed, and the results are presented to validate the effectiveness of proposed approach. According to the results in the robustness function, hub energy system has become robust against 24.6% more price, while total operation cost of system has been increased 2.8%. Also, hub system has gained 75 $ economic benefit due to the reduction of price in the opportunity function.

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References

  1. 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 

  2. 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 

  3. 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 

  4. Nojavan, S., Majidi, M., & Zare, K. (2017). Performance improvement of a battery/PV/fuel cell/grid hybrid energy system considering load uncertainty modeling using IGDT. Energy Conversion and Management, 147, 29–39. https://doi.org/10.1016/j.enconman.2017.05.039.

    Article  Google Scholar 

  5. 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 

  6. Nojavan, S., Zare, K., & Feyzi, M. R. (2013). Optimal bidding strategy of generation station in power market using information gap decision theory (IGDT). Electric Power Systems Research, 96, 56–63. https://doi.org/10.1016/j.epsr.2012.10.006.

    Article  Google Scholar 

  7. 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 

  8. 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.

    Google Scholar 

  9. 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 

  10. 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 

  11. Nojavan, S., Majidi, M., Najafi-Ghalelou, A., Ghahramani, M., & Zare, K. (2017). A cost-emission model for fuel cell/PV/battery hybrid energy system in the presence of demand response program: ε-constraint method and fuzzy satisfying approach. Energy Conversion and Management, 138, 383–392.

    Article  Google Scholar 

  12. 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 

  13. Najafi-Ghalelou, A., Nojavan, S., Majidi, M., Jabari, F., & Zare, K. (2018). Solar thermal energy storage for residential sector. In Operation, planning, and analysis of energy storage systems in smart energy hubs (pp. 79–101). Cham: Springer.

    Chapter  Google Scholar 

  14. 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 

  15. 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 

  16. 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 

  17. Ghalelou, A. N., Fakhri, A. P., Nojavan, S., Majidi, M., & Hatami, H. (2016). A stochastic self-scheduling program for compressed air energy storage (CAES) of renewable energy sources (RESs) based on a demand response mechanism. Energy Conversion and Management, 120, 388–396.

    Article  Google Scholar 

  18. 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 

  19. 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 

  20. 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 

  21. 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 

  22. 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 

  23. 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 

  24. Parisio, A., Del Vecchio, C., & Vaccaro, A. (2012). A robust optimization approach to energy hub management. International Journal of Electrical Power & Energy Systems, 42(1), 98–104. https://doi.org/10.1016/j.ijepes.2012.03.015.

    Article  Google Scholar 

  25. 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 

  26. 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 

  27. 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 

  28. 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 

  29. Bahrami, S., & Sheikhi, A. (2016). From demand response in smart grid toward integrated demand response in smart energy hub. IEEE Transactions on Smart Grid, 7(2), 650–658.

    Google Scholar 

  30. Gazijahani, F. S., & Salehi, J. (2018). Integrated DR and reconfiguration scheduling for optimal operation of microgrids using Hong’s point estimate method. International Journal of Electrical Power & Energy Systems, 99, 481–492.

    Article  Google Scholar 

  31. Althaher, S., Mancarella, P., & Mutale, J. (2015). Automated demand response from home energy management system under dynamic pricing and power and comfort constraints. IEEE Transactions on Smart Grid, 6(4), 1874–1883.

    Article  Google Scholar 

  32. Bozchalui, M. C., Hashmi, S. A., Hassen, H., Cañizares, C. A., & Bhattacharya, K. (2012). Optimal operation of residential energy hubs in smart grids. IEEE Transactions on Smart Grid, 3(4), 1755–1766.

    Article  Google Scholar 

  33. 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 

  34. Tavakoli, M., Shokridehaki, F., Akorede, M. F., Marzband, M., Vechiu, I., & Pouresmaeil, E. (2018). CVaR-based energy management scheme for optimal resilience and operational cost in commercial building microgrids. International Journal of Electrical Power & Energy Systems, 100, 1–9.

    Article  Google Scholar 

  35. Gazijahani, F. S., Ravadanegh, S. N., & Salehi, J. (2018). Stochastic multi-objective model for optimal energy exchange optimization of networked microgrids with presence of renewable generation under risk-based strategies. ISA Transactions, 73, 100–111.

    Article  Google Scholar 

  36. 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 

  37. 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 

  38. 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 

  39. 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 

  40. 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 

  41. 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 

  42. Marzband, M., Fouladfar, M. H., Akorede, M. F., Lightbody, G., & Pouresmaeil, E. (2018). Framework for smart transactive energy in home-microgrids considering coalition formation and demand side management. Sustainable Cities and Society, 40, 136–154.

    Article  Google Scholar 

  43. Marzband, M., Azarinejadian, F., Savaghebi, M., Pouresmaeil, E., Guerrero, J. M., & Lightbody, G. (2018). Smart transactive energy framework in grid-connected multiple home microgrids under independent and coalition operations. Renewable Energy, 126, 95–106.

    Article  Google Scholar 

  44. Marzband, M., Javadi, M., Pourmousavi, S. A., & Lightbody, G. (2018). An advanced retail electricity market for active distribution systems and home microgrid interoperability based on game theory. Electric Power Systems Research, 157, 187–199.

    Article  Google Scholar 

  45. 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 

  46. 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 

  47. 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 

  48. Evins, R. (2015). Multi-level optimization of building design, energy system sizing and operation. Energy, 90, 1775–1789. https://doi.org/10.1016/j.energy.2015.07.007.

    Article  Google Scholar 

  49. 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 

  50. 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 

  51. 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 

  52. 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 

  53. 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 

  54. 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 

  55. 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 

  56. 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 

  57. 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.

    Google Scholar 

  58. 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 

  59. 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 

  60. 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 

  61. 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 

  62. 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 

  63. 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 

  64. Nojavan, S., Zare, K., & Mohammadi-Ivatloo, B. (2017). Risk-based framework for supplying electricity from renewable generation-owning retailers to price-sensitive customers using information gap decision theory. International Journal of Electrical Power & Energy Systems, 93, 156–170.

    Article  Google Scholar 

  65. Nojavan, S., Zare, K., & Mohammadi-Ivatloo, B. (2017). Information gap decision theory-based risk-constrained bidding strategy of price-taker GenCo in joint energy and reserve markets. Electric Power Components and Systems, 45(1), 49–62.

    Article  Google Scholar 

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

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Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

    Majid Majidi & Kazem Zare

  2. Department of Electrical Engineering, University of Bonab, Bonab, Iran

    Sayyad Nojavan

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  1. Majid Majidi
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  2. Sayyad Nojavan
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Correspondence to Majid Majidi .

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Editors and Affiliations

  1. Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

    Behnam Mohammadi-ivatloo

  2. Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

    Morteza Nazari-Heris

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Majidi, M., Nojavan, S., Zare, K. (2019). Optimization Framework Based on Information Gap Decision Theory for Optimal Operation of Multi-energy Systems. 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_3

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  • DOI: https://doi.org/10.1007/978-3-030-04296-7_3

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