A data-driven viable supply network for energy security and economic prosperity

被引:5
|
作者
Mun, Kwon Gi [1 ]
Cai, Wenbo [2 ]
Rodgers, Mark [3 ]
Choi, Sungyong [4 ]
机构
[1] Calif State Polytech Univ Pomona, Technol & Operat Management, Pomona, CA USA
[2] New Jersey Inst Technol, Mech & Ind Engn, Newark, NJ USA
[3] State Univ New Jersey Newark & New Brunswick, Supply Chain Management, Newark, NJ 08901 USA
[4] Hanyang Univ, Sch Business Operat & Serv Management, Seoul, South Korea
关键词
Viable energy; renewable; fossil fuel; supply chain disruptions; energy economics; CHAIN; POWER; OPTIMIZATION; SYSTEMS; GENERATION; VIABILITY;
D O I
10.1080/00207543.2023.2254414
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Developing countries face significant challenges in achieving energy stability due to disruptions in their energy supply chains, which jeopardise their sustainability and survivability. Addressing these issues requires careful consideration of public funding in the energy sector and the design of a robust electricity infrastructure. This study aims to identify long-term infrastructure investment strategies that can strengthen the viability of the energy supply network in developing countries, even with limited public funds. The research is underpinned by an empirical study that shows the successful development of energy infrastructure through the effective implementation of viable energy strategies in developing countries. By adopting these strategies, energy supply networks in developing countries can mitigate supply disruptions and avert economic losses. Further, this study evaluates the effectiveness of the viable energy supply network by incorporating a mix of energy resources based on actual data from Pakistan. The contributions of the study are as follows. We develop a viable energy supply network model that considers crucial elements, such as extraction, transportation, generation, transmission, and supply and facilities decision-making. Additionally, we show that the performance of the energy network is not only driven by electricity supply but also influenced by the overall economic growth of the country.
引用
收藏
页码:8988 / 9010
页数:23
相关论文
共 50 条
  • [31] Parametric Majorization for Data-Driven Energy Minimization Methods
    Geiping, Jonas
    Moeller, Michael
    2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019), 2019, : 10261 - 10272
  • [32] Data-Driven Predictive Control of Building Energy Consumption under the IoT Architecture
    Ke, Ji
    Qin, Yude
    Wang, Biao
    Yang, Shundong
    Wu, Hao
    Yang, Hang
    Zhao, Xing
    WIRELESS COMMUNICATIONS & MOBILE COMPUTING, 2020, 2020 (2020):
  • [33] Data-Driven Optimization of Piezoelectric Energy Harvesters via Pattern Search Algorithm
    Huang, Yang
    Yi, Zhiran
    Hu, Guosheng
    Yang, Bin
    MICROMACHINES, 2021, 12 (05)
  • [34] Data-driven district energy management with surrogate models and deep reinforcement learning
    Pinto, Giuseppe
    Deltetto, Davide
    Capozzoli, Alfonso
    APPLIED ENERGY, 2021, 304
  • [35] A Data-driven Distributionally Robust Operational Model for Urban Integrated Energy Systems
    Gao, Hongjun
    Liu, Zhenyu
    Liu, Youbo
    Wang, Lingfeng
    Liu, Junyong
    CSEE JOURNAL OF POWER AND ENERGY SYSTEMS, 2022, 8 (03): : 789 - 800
  • [36] An integrated, systematic data-driven supply-demand side management method for smart integrated energy systems
    Su, Huai
    Chi, Lixun
    Zio, Enrico
    Li, Zhenlin
    Fan, Lin
    Yang, Zhe
    Liu, Zhe
    Zhang, Jinjun
    ENERGY, 2021, 235
  • [37] Data-driven prediction of change propagation using Dependency Network
    Lee, Jihwan
    Hong, Yoo S.
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2018, 70 : 149 - 158
  • [38] A data-driven bi-objective matheuristic for energy-optimising timetables in a passenger railway network
    Als, Matthias Villads Hinsch
    Madsen, Mathias Bejlegaard
    Jensen, Rune Moller
    JOURNAL OF RAIL TRANSPORT PLANNING & MANAGEMENT, 2023, 26
  • [39] A systematic and network-based analysis of data-driven quality management in supply chains and proposed future research directions
    Agrawal, Rohit
    Wankhede, Vishal Ashok
    Kumar, Anil
    Luthra, Sunil
    TQM JOURNAL, 2023, 35 (01): : 73 - 101
  • [40] Design of distributionally robust closed-loop supply chain network based on data-driven under disruption risks
    Zhao, Bing
    Su, Ke
    Wei, Yanshu
    Shang, Tianyou
    INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE-OPERATIONS & LOGISTICS, 2024, 11 (01)