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Transshipment model-based MILP (mixed-integer linear programming) formulation for targeting and design of hybrid power systems

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  • Chen, Cheng-Liang
  • Lai, Chieh-Ting
  • Lee, Jui-Yuan

Abstract

A HPS (hybrid power system) includes various sources of RE (renewable electricity) such as wind, solar, and biomass, and is supplemented by the public grid should the total RE supply is deficit to total demand. Given the operational capability of the total RE supply and the time-dependent demand rate from various appliances, and also given the recovery ratio of the electricity due to storage and discharge cycle, a mathematical programming approach is established for the analysis and design of the off-grid HPS. A CTM (condensed transshipment model) is first proposed, and the problem of targeting and analysis of the HPS problem is formulated as a MILP (mixed-integer linear programming) model. The CTM-based MILP formulation is then extended to the ETM (expanded transshipment model), where further detailed examination such as allocating the power sources to demands is included and is also expressed as a mixed-integer linear programming model, for providing more detailed source-sink matching information. An illustrative case study taken from Ref. [1] is studied to demonstrate the proposed transshipment models for elucidation of the hybrid power system.

Suggested Citation

  • Chen, Cheng-Liang & Lai, Chieh-Ting & Lee, Jui-Yuan, 2014. "Transshipment model-based MILP (mixed-integer linear programming) formulation for targeting and design of hybrid power systems," Energy, Elsevier, vol. 65(C), pages 550-559.
  • Handle: RePEc:eee:energy:v:65:y:2014:i:c:p:550-559
    DOI: 10.1016/j.energy.2013.11.021
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    1. Mohammad Rozali, Nor Erniza & Wan Alwi, Sharifah Rafidah & Abdul Manan, Zainuddin & Klemeš, Jiří Jaromír & Hassan, Mohammad Yusri, 2013. "Process integration of hybrid power systems with energy losses considerations," Energy, Elsevier, vol. 55(C), pages 38-45.
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    4. Tan, Raymond R. & Foo, Dominic C.Y., 2007. "Pinch analysis approach to carbon-constrained energy sector planning," Energy, Elsevier, vol. 32(8), pages 1422-1429.
    5. Wan Alwi, Sharifah Rafidah & Mohammad Rozali, Nor Erniza & Abdul-Manan, Zainuddin & Klemeš, Jiří Jaromír, 2012. "A process integration targeting method for hybrid power systems," Energy, Elsevier, vol. 44(1), pages 6-10.
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    Cited by:

    1. Lee, Jui-Yuan & Aviso, Kathleen B. & Tan, Raymond R., 2019. "Multi-objective optimisation of hybrid power systems under uncertainties," Energy, Elsevier, vol. 175(C), pages 1271-1282.
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    3. Mohammad Rozali, Nor Erniza & Wan Alwi, Sharifah Rafidah & Manan, Zainuddin Abdul & Klemeš, Jiří Jaromír, 2016. "Process Integration for Hybrid Power System supply planning and demand management – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 834-842.
    4. Liu, X.G. & He, C. & He, C.C. & Chen, J.J. & Zhang, B.J. & Chen, Q.L., 2017. "A new retrofit approach to the absorption-stabilization process for improving energy efficiency in refineries," Energy, Elsevier, vol. 118(C), pages 1131-1145.
    5. Giaouris, Damian & Papadopoulos, Athanasios I. & Patsios, Charalampos & Walker, Sara & Ziogou, Chrysovalantou & Taylor, Phil & Voutetakis, Spyros & Papadopoulou, Simira & Seferlis, Panos, 2018. "A systems approach for management of microgrids considering multiple energy carriers, stochastic loads, forecasting and demand side response," Applied Energy, Elsevier, vol. 226(C), pages 546-559.
    6. Abhi Chatterjee & Daniel Burmester & Alan Brent & Ramesh Rayudu, 2019. "Research Insights and Knowledge Headways for Developing Remote, Off-Grid Microgrids in Developing Countries," Energies, MDPI, vol. 12(10), pages 1-19, May.
    7. Silvente, Javier & Aguirre, Adrián M. & Zamarripa, Miguel A. & Méndez, Carlos A. & Graells, Moisès & Espuña, Antonio, 2015. "Improved time representation model for the simultaneous energy supply and demand management in microgrids," Energy, Elsevier, vol. 87(C), pages 615-627.
    8. Jiang, Yinghua & Kang, Lixia & Liu, Yongzhong, 2020. "Optimal configuration of battery energy storage system with multiple types of batteries based on supply-demand characteristics," Energy, Elsevier, vol. 206(C).
    9. Nadjemi, O. & Nacer, T. & Hamidat, A. & Salhi, H., 2017. "Optimal hybrid PV/wind energy system sizing: Application of cuckoo search algorithm for Algerian dairy farms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1352-1365.
    10. Jiang, Yinghua & Kang, Lixia & Liu, Yongzhong, 2019. "A unified model to optimize configuration of battery energy storage systems with multiple types of batteries," Energy, Elsevier, vol. 176(C), pages 552-560.
    11. Theo, Wai Lip & Lim, Jeng Shiun & Wan Alwi, Sharifah Rafidah & Mohammad Rozali, Nor Erniza & Ho, Wai Shin & Abdul-Manan, Zainuddin, 2016. "An MILP model for cost-optimal planning of an on-grid hybrid power system for an eco-industrial park," Energy, Elsevier, vol. 116(P2), pages 1423-1441.
    12. Chen, Cheng-Liang & Lai, Chieh-Ting & Lee, Jui-Yuan, 2014. "Transshipment model-based linear programming formulation for targeting hybrid power systems with power loss considerations," Energy, Elsevier, vol. 75(C), pages 24-30.
    13. Norbu, Sonam & Bandyopadhyay, Santanu, 2017. "Power Pinch Analysis for optimal sizing of renewable-based isolated system with uncertainties," Energy, Elsevier, vol. 135(C), pages 466-475.
    14. Mandelli, Stefano & Barbieri, Jacopo & Mereu, Riccardo & Colombo, Emanuela, 2016. "Off-grid systems for rural electrification in developing countries: Definitions, classification and a comprehensive literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1621-1646.

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