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Power supply chain network design problem for smart grid considering differential pricing and buy-back policies

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  • Tsao, Yu-Chung
  • Vu, Thuy-Linh

Abstract

With the rising sense of environmental consciousness, the development of renewable energy and rapid technological innovation have become drivers, as well as posed challenges, for the power supply chain network. This study addresses the smart power supply chain network design problem considering two players: the electric power company and the users. Using distributed generations (DGs) such as solar, wind, and biomass, among others, users can generate their own renewable energy. Here, differential pricing and buy-back policies maximize benefits for both the companies and users. Under the buy-back contract, users who own DGs can generate renewable electricity, determine the electricity they need, and buy from or sell their share to the electric company. The continuous approximation approach is used to model resolutions for smart power supply chain network problems. Algorithms based on non-linear optimization are proposed to solve the smart power supply chain network design problems for two cases: centralized and decentralized models. Finally, a numerical analysis illustrates the solution procedures and examines the effects of dynamic parameters on decision-making. The results show that the centralized model obtains a higher profit than the decentralized model. Further, the results of the numerical analysis can serve as references for business managers or administrators.

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  • Tsao, Yu-Chung & Vu, Thuy-Linh, 2019. "Power supply chain network design problem for smart grid considering differential pricing and buy-back policies," Energy Economics, Elsevier, vol. 81(C), pages 493-502.
    • Handle: RePEc:eee:eneeco:v:81:y:2019:i:c:p:493-502
    DOI: 10.1016/j.eneco.2019.04.022
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    Cited by:

    1. Yu-Chung Tsao & Thuy-Linh Vu, 2023. "Electricity pricing, capacity, and predictive maintenance considering reliability," Annals of Operations Research, Springer, vol. 322(2), pages 991-1011, March.
    2. Tsao, Yu-Chung & Beyene, Tsehaye Dedimas & Thanh, Vo-Van & Gebeyehu, Sisay Geremew & Kuo, Tsai-Chi, 2022. "Power distribution network design considering the distributed generations and differential and dynamic pricing," Energy, Elsevier, vol. 241(C).
    3. Jamali, Mohammad-Bagher & Rasti-Barzoki, Morteza & Khosroshahi, Hossein & Altmann, Jörn, 2022. "An evolutionary game-theoretic approach to study the technological transformation of the industrial sector toward renewable electricity procurement: A case study of Iran," Applied Energy, Elsevier, vol. 318(C).
    4. Nesrin Ada & Manavalan Ethirajan & Anil Kumar & Vimal K.E.K & Simon Peter Nadeem & Yigit Kazancoglu & Jayakrishna Kandasamy, 2021. "Blockchain Technology for Enhancing Traceability and Efficiency in Automobile Supply Chain—A Case Study," Sustainability, MDPI, vol. 13(24), pages 1-21, December.
    5. Tsao, Yu-Chung & Thanh, Vo-Van & Lu, Jye-Chyi, 2022. "Efficiency of resilient three-part tariff pricing schemes in residential power markets," Energy, Elsevier, vol. 239(PD).
    6. Bo Yan & Yanping Liu & Zijie Jin, 2023. "Joint coordination contract for capital‐constrained supply chains under asymmetric information," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(1), pages 251-270, January.

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