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Supply chain intelligence for electricity markets: A smart grid perspective

Author

Listed:
  • Jelena Lukić

    (Public Enterprise Elektromreža Srbije)

  • Miloš Radenković

    (Union University)

  • Marijana Despotović-Zrakić

    (University of Belgrade)

  • Aleksandra Labus

    (University of Belgrade)

  • Zorica Bogdanović

    (University of Belgrade)

Abstract

Smart grid technologies are bringing innovations in electrical power industries, affecting all parts of the electricity supply chain, and leading to changes in market structure, business models and services. In this paper we introduce a model of business intelligence for a smart grid supply chain. The model is developed in order to provide electricity markets with the necessary data flows and information important for the decision making process. The proposed model offers a way to efficiently leverage the new metering architecture and the new capabilities of the grid to reap immediate business value from the huge amounts of disparate data in emerging smart grids. The model was evaluated for the Serbian electricity market in the electric power transmission company Public Enterprise “Elektromreža Srbije”. The results show that business intelligence solutions can contribute to a more effective management of smart grids, in order to ensure that companies achieve sustainability in the increasingly competitive electricity markets, while still providing the high quality services to end users.

Suggested Citation

  • Jelena Lukić & Miloš Radenković & Marijana Despotović-Zrakić & Aleksandra Labus & Zorica Bogdanović, 2017. "Supply chain intelligence for electricity markets: A smart grid perspective," Information Systems Frontiers, Springer, vol. 19(1), pages 91-107, February.
    • Handle: RePEc:spr:infosf:v:19:y:2017:i:1:d:10.1007_s10796-015-9592-z
    DOI: 10.1007/s10796-015-9592-z
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    References listed on IDEAS

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    1. Koliba, Christopher & DeMenno, Mercy & Brune, Nancy & Zia, Asim, 2014. "The salience and complexity of building, regulating, and governing the smart grid: Lessons from a statewide public–private partnership," Energy Policy, Elsevier, vol. 74(C), pages 243-252.
    2. Lund, Peter D., 2014. "How fast can businesses in the new energy sector grow? An analysis of critical factors," Renewable Energy, Elsevier, vol. 66(C), pages 33-40.
    3. Erdinc, Ozan & Paterakis, Nikolaos G. & Pappi, Iliana N. & Bakirtzis, Anastasios G. & Catalão, João P.S., 2015. "A new perspective for sizing of distributed generation and energy storage for smart households under demand response," Applied Energy, Elsevier, vol. 143(C), pages 26-37.
    4. Su, Wencong & Huang, Alex Q., 2014. "A game theoretic framework for a next-generation retail electricity market with high penetration of distributed residential electricity suppliers," Applied Energy, Elsevier, vol. 119(C), pages 341-350.
    5. Personal, Enrique & Guerrero, Juan Ignacio & Garcia, Antonio & Peña, Manuel & Leon, Carlos, 2014. "Key performance indicators: A useful tool to assess Smart Grid goals," Energy, Elsevier, vol. 76(C), pages 976-988.
    6. Bae, Mungyu & Kim, Hwantae & Kim, Eugene & Chung, Albert Yongjoon & Kim, Hwangnam & Roh, Jae Hyung, 2014. "Toward electricity retail competition: Survey and case study on technical infrastructure for advanced electricity market system," Applied Energy, Elsevier, vol. 133(C), pages 252-273.
    7. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2015. "A clustering approach to domestic electricity load profile characterisation using smart metering data," Applied Energy, Elsevier, vol. 141(C), pages 190-199.
    8. Arends, Marcel & Hendriks, Paul H.J., 2014. "Smart grids, smart network companies," Utilities Policy, Elsevier, vol. 28(C), pages 1-11.
    9. Jacqueline Corbett, 2013. "Using information systems to improve energy efficiency: Do smart meters make a difference?," Information Systems Frontiers, Springer, vol. 15(5), pages 747-760, November.
    10. Vardakas, John S. & Zorba, Nizar & Verikoukis, Christos V., 2015. "Performance evaluation of power demand scheduling scenarios in a smart grid environment," Applied Energy, Elsevier, vol. 142(C), pages 164-178.
    11. Giordano, Vincenzo & Fulli, Gianluca, 2012. "A business case for Smart Grid technologies: A systemic perspective," Energy Policy, Elsevier, vol. 40(C), pages 252-259.
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    Cited by:

    1. Rodgers, Waymond & Cardenas, Jesus A. & Gemoets, Leopoldo A. & Sarfi, Robert J., 2023. "A smart grids knowledge transfer paradigm supported by experts' throughput modeling artificial intelligence algorithmic processes," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    2. Shivam Gupta & Vinayak A. Drave & Surajit Bag & Zongwei Luo, 2019. "Leveraging Smart Supply Chain and Information System Agility for Supply Chain Flexibility," Information Systems Frontiers, Springer, vol. 21(3), pages 547-564, June.

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