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A linearization and parameterization approach to tri-objective linear programming problems for power generation expansion planning

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  • Chen, Fang
  • Huang, Guohe
  • Fan, Yurui

Abstract

The present study proposed a new solution to a tri-objective linear programming problem for generation expansion planning by converting the tri-objective linear programming problem (i.e. simultaneously maximizing the total power generation, minimizing the total system cost, and minimizing the total CO2 emission) into an equivalent bi-objective linear fractional programming problem (i.e. simultaneously maximizing the ratio of the total power generation to the total system cost, and the ratio of the total power generation to the total CO2 emission) to produce a better nondominated solution without any preference information from a decision maker. An approach for solving the bi-objective linear fractional programming problem is a newly developed linearization and parameterization approach based on Dinkelbach's theorem and Güzel's approach, which transforms all of linear fractional objective functions into a single objective linear programming problem. The proposed bi-objective fractional programming method was applied to a case study of power generation expansion planning problem. Moreover, comparison of the solutions generated by the proposed linearization and parameterization approach and a traditional weighted sum approach has been conducted to demonstrate the effectiveness of the proposed approach in reflecting the trade-offs among the total power generation, the total system cost and the total CO2 emission.

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  • Chen, Fang & Huang, Guohe & Fan, Yurui, 2015. "A linearization and parameterization approach to tri-objective linear programming problems for power generation expansion planning," Energy, Elsevier, vol. 87(C), pages 240-250.
    • Handle: RePEc:eee:energy:v:87:y:2015:i:c:p:240-250
    DOI: 10.1016/j.energy.2015.04.104
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    4. Rego, Erik Eduardo & Costa, Oswaldo L.V. & Ribeiro, Celma de Oliveira & Lima Filho, Roberto Ivo da R. & Takada, Hellinton & Stern, Julio, 2020. "The trade-off between demand growth and renewables: A multiperiod electricity planning model under CO2 emission constraints," Energy, Elsevier, vol. 213(C).
    5. Jornada, Daniel & Leon, V. Jorge, 2016. "Robustness methodology to aid multiobjective decision making in the electricity generation capacity expansion problem to minimize cost and water withdrawal," Applied Energy, Elsevier, vol. 162(C), pages 1089-1108.
    6. Xiangyu Kong & Jingtao Yao & Zhijun E & Xin Wang, 2019. "Generation Expansion Planning Based on Dynamic Bayesian Network Considering the Uncertainty of Renewable Energy Resources," Energies, MDPI, vol. 12(13), pages 1-20, June.
    7. Niu, Geng & Zheng, Yi & Han, Feng & Qin, Huapeng, 2019. "The nexus of water, ecosystems and agriculture in arid areas: A multiobjective optimization study on system efficiencies," Agricultural Water Management, Elsevier, vol. 223(C), pages 1-1.
    8. Chen, F. & Huang, G.H. & Fan, Y.R. & Chen, J.P., 2017. "A copula-based fuzzy chance-constrained programming model and its application to electric power generation systems planning," Applied Energy, Elsevier, vol. 187(C), pages 291-309.
    9. Zhang, X.Y. & Huang, G.H. & Zhu, H. & Li, Y.P., 2017. "A fuzzy-stochastic power system planning model: Reflection of dual objectives and dual uncertainties," Energy, Elsevier, vol. 123(C), pages 664-676.
    10. Rodgers, Mark D. & Coit, David W. & Felder, Frank A. & Carlton, Annmarie, 2018. "Generation expansion planning considering health and societal damages – A simulation-based optimization approach," Energy, Elsevier, vol. 164(C), pages 951-963.
    11. Song, Tangnyu & Huang, Guohe & Zhou, Xiong & Wang, Xiuquan, 2018. "An inexact two-stage fractional energy systems planning model," Energy, Elsevier, vol. 160(C), pages 275-289.
    12. Yu, L. & Li, Y.P. & Huang, G.H., 2016. "A fuzzy-stochastic simulation-optimization model for planning electric power systems with considering peak-electricity demand: A case study of Qingdao, China," Energy, Elsevier, vol. 98(C), pages 190-203.
    13. Shornalatha Euttamarajah & Yin Hoe Ng & Chee Keong Tan, 2021. "Energy-Efficient Joint Base Station Switching and Power Allocation for Smart Grid Based Hybrid-Powered CoMP-Enabled HetNet," Future Internet, MDPI, vol. 13(8), pages 1-22, August.
    14. Kao, Han-Ying & Wu, Dong-Jyun & Huang, Chia-Hui, 2017. "Evaluation of cloud service industry with dynamic and network DEA models," Applied Mathematics and Computation, Elsevier, vol. 315(C), pages 188-202.
    15. Xiangyu Kong & Siqiong Zhang & Bowei Sun & Qun Yang & Shupeng Li & Shijian Zhu, 2020. "Research on Home Energy Management Method for Demand Response Based on Chance-Constrained Programming," Energies, MDPI, vol. 13(11), pages 1-27, June.

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