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A general source-sink model with inoperability constraints for robust energy sector planning

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  • Tan, Raymond R.

Abstract

The concept of inoperability was originally introduced as a means of quantifying risk in systems comprised of interdependent subsystems, using a modified input–output framework. This paper describes a novel robust optimization model for energy planning with inoperability constraints. The formulation is based on the established source-sink framework, which has been used extensively for energy planning applications under various environmental footprint constraints. The proposed model determines the optimal allocation of various energy sources within a system to corresponding energy sinks or demands, while ensuring that inoperability limits of the latter are satisfied for multiple enumerated scenarios. The basic formulation results in a linear program (LP), while a mixed integer linear programming (MILP) extension is also described. In either case, a globally optimal solution can be easily determined if one exists. Illustrative case studies are then given to demonstrate this new method.

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  • Tan, Raymond R., 2011. "A general source-sink model with inoperability constraints for robust energy sector planning," Applied Energy, Elsevier, vol. 88(11), pages 3759-3764.
    • Handle: RePEc:eee:appene:v:88:y:2011:i:11:p:3759-3764
    DOI: 10.1016/j.apenergy.2011.04.016
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    Cited by:

    1. Inglesi-Lotz, Roula & Blignaut, James N., 2011. "South Africa’s electricity consumption: A sectoral decomposition analysis," Applied Energy, Elsevier, vol. 88(12), pages 4779-4784.
    2. Bentsen, Niclas Scott & Jack, Michael W. & Felby, Claus & Thorsen, Bo Jellesmark, 2014. "Allocation of biomass resources for minimising energy system greenhouse gas emissions," Energy, Elsevier, vol. 69(C), pages 506-515.
    3. Martinez-Hernandez, Elias & Sadhukhan, Jhuma & Campbell, Grant M., 2013. "Integration of bioethanol as an in-process material in biorefineries using mass pinch analysis," Applied Energy, Elsevier, vol. 104(C), pages 517-526.
    4. Kasivisvanathan, Harresh & Barilea, Ivan Dale U. & Ng, Denny K.S. & Tan, Raymond R., 2013. "Optimal operational adjustment in multi-functional energy systems in response to process inoperability," Applied Energy, Elsevier, vol. 102(C), pages 492-500.
    5. Al-Mayyahi, Mohmmad A. & Hoadley, Andrew F.A. & Rangaiah, G.P., 2013. "A novel graphical approach to target CO2 emissions for energy resource planning and utility system optimization," Applied Energy, Elsevier, vol. 104(C), pages 783-790.
    6. He, Peijun & Ng, Tsan Sheng & Su, Bin, 2017. "Energy-economic recovery resilience with Input-Output linear programming models," Energy Economics, Elsevier, vol. 68(C), pages 177-191.
    7. Foo, Dominic C.Y. & Tan, Raymond R. & Lam, Hon Loong & Abdul Aziz, Mustafa Kamal & Klemeš, Jiří Jaromír, 2013. "Robust models for the synthesis of flexible palm oil-based regional bioenergy supply chain," Energy, Elsevier, vol. 55(C), pages 68-73.
    8. Jui-Yuan Lee & Han-Fu Lin, 2019. "Multi-Footprint Constrained Energy Sector Planning," Energies, MDPI, vol. 12(12), pages 1-18, June.
    9. Aristotle T. Ubando & Isidro Antonio V. Marfori & Kathleen B. Aviso & Raymond R. Tan, 2019. "Optimal Operational Adjustment of a Community-Based Off-Grid Polygeneration Plant using a Fuzzy Mixed Integer Linear Programming Model," Energies, MDPI, vol. 12(4), pages 1-17, February.
    10. Krista Danielle S. Yu & Kathleen B. Aviso & Michael Angelo B. Promentilla & Joost R. Santos & Raymond R. Tan, 2016. "A weighted fuzzy linear programming model in economic input–output analysis: an application to risk management of energy system disruptions," Environment Systems and Decisions, Springer, vol. 36(2), pages 183-195, June.

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