Skip to main content
SpringerLink
Log in

Supply chain intelligence for electricity markets: A smart grid perspective

  • Published:
Information Systems Frontiers Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Accenture (2011): Ten Leading Practices for Smart Grid Analytics. http://www.accenture.com/SiteCollectionDocuments/PDF/WSS101_SmartGridAnalytics_A4.pdf. Accessed 28 May 2015.

  • AERS (2014) Agencija za energetiku Republike Srbije: 2013 Energy Agency Report - Serbian Energy Sector Report/Annual and Financial Report. http://www.aers.rs/Files/Izvestaji/Godisnji/Eng/AERS%20Annual%20Report%202013.pdf. Accessed 28 May 2015

  • Alhyasat, E. B., & Al-Dalahmeh, M. (2013). Data warehouse success and strategic oriented business intelligence. A Theoretical Framework, Journal of Management Research, 5(3), 169–184.

    Google Scholar 

  • Arends, M., & Hendriks, P. H. J. (2014). Smart grids, smart network companies. Utilities Policy, 28, 1–11.

    Article  Google Scholar 

  • Argotte, L., Mejia-Lavalle, M., Sosa, R. (2009). Business Intelligence and Energy Markets: A survey, in: 15th International Conference on Intelligent System Applications to Power Systems, ISAP ‘09. 2009, (pp. 1–6), ISBN 978–1–4244–5097–8

  • Bae, M., Kim, H., Kim, E., Chung, A. Y., Kim, H., & Roh, J. H. (2014). Toward electricity retail competition: Survey and case study on technical infrastructure for advanced electricity market system. Applied Energy, 15, 252–273.

    Article  Google Scholar 

  • Babic, J. (2013): Agent-based Modeling of Electricity Markets in a Smart Grid Environment. https://www.fer.hr/_download/repository/KDI_Jurica_Babic.pdf. Accessed 28 May 2015.

  • Bećirović, E., Musić, M., Hasanspahić, N., & Avdaković, S. (2014). Smart grid implementation in electricity distribution of elektroprivreda B&H – dequirements and objectives. Balkan Journal of Electrical and Computer Engineering, 2(3), 100–103.

    Google Scholar 

  • Bremser, W. G., & Chung, Q. B. (2005). A framework for performance measurement in the e-business environment. Electronic Commerce Research and Applications, 4(4), 395–412.

    Article  Google Scholar 

  • Corbett, J. (2013). Using information systems to improve energy efficiency: Do smart meters make a difference? Information Systems Frontiers, 15(5), 747–760.

    Article  Google Scholar 

  • Daily Auction Rules for the allocation of transmission capacities at the border of control areas of JP Elektromreža Srbije (“EMS”) and CN Translelectrica S.A. (“Transelectrica”) (2013): PE “Elektromreža Srbije“.

  • http://www.ems.rs/media/uploads/2013/11/RO-RS_Daily_Auction_Rules_2014RO1.pdf?language=lat. Accesed 14 August 2015.

  • Diamantoulakis, P., Kapinas, V., & Karagiannidisa, G. (2015). Big data analytics for dynamic energy management in smart grids. Big Data Research, 2(3), 94–101.

    Article  Google Scholar 

  • Dupont, B. Meeus, L., Belmans, R. (2010). Measuring the "smartness" of the electricity grid. in: Energy Market (EEM), 7th International Conference on the European. IEEE, (pp. 1–6), ISBN 978–1–4244–6838–6

  • EPRI (2003): Transmission System Reliability Performance Metrics Requirements (Technical Update report). Palo Alto, California: Electric Power Research Institute, Inc. http://www.bluearcenergy.com/TransmissionSystemReliabilityPerformanceMetricsRequirements.pdf. Accessed 28 May 2015.

  • Erdinc, O., Paterakis, N., Pappi, I., Bakirtzis, A., & Catalão, J. (2015). A new perspective for sizing of distributed generation and energy storage for smart households under demand response. Applied Energy, 143, 26–37.

    Article  Google Scholar 

  • Fang, X., Misra, S., Xue, G., & Yang, D. (2012). Smart grid - the new and improved power grid: A survey. IEEE Communications Surveys & Tutorials, 14(4), 944–980.

    Article  Google Scholar 

  • Farhangi, H. (2010). The path of the smart grid. IEEE Power and Energy Magazine, 8(1), 18–28.

    Article  Google Scholar 

  • Ferreira, R., Silva, J., Strauhs, F., & Soares, A. (2011). Performance management in collaborative networks: A methodological proposal. Journal of Universal Computer Science, 17(10), 1412–1429.

    Google Scholar 

  • Ghanem, D. A., & Mander, S. (2014). Designing consumer engagement with the smart grids of the future: bringing active demand technology to everyday life. Technology Analysis & Strategic Management, 26(10), 1163–1175.

    Article  Google Scholar 

  • Giordano, V., & Fulli, G. (2012). A business case for smart grid technologies: a systemic perspective. Energy Policy, 40, 252–259.

    Google Scholar 

  • Gungor, V. C., Sahin, D., Kocak, T., Ergut, S., Buccella, C., Cecati, C., & Hancke, G. P. (2011). Smart grid technologies: Communication technologies and standards. IEEE Transactions on Industrial Informatics, 7(4), 529–539.

    Article  Google Scholar 

  • Hedin, M., Wheelock, C. (2010). Executive Summary: Smart Grid Data Analytics Carbon Management Software and Services Business Intelligence, Situational Awareness, and Predictive Analytics for Utility Customer Information and Grid Operations: Market Analysis and Forecasts. Research Report. Pike Research LLC.

  • IEA (2011). International Energy Agency. Empowering Customer Choice in Electricity Markets. http://www.iea.org/publications/freepublications/publication/empower.pdf. Accessed 28 May 2015.

  • IEEE Smart Grid (2014). Smart Grid Conceptual Model. http://smartgrid.ieee.org/education/1048-smart-grid-101-with-erich-gunther. Accessed 28 May 2015.

  • Ipakchi, A. (2007). Implementing the smart grid: Enterprise Information Integration, Grid-Interop Forum 2007. USA:Albuquerque.

    Google Scholar 

  • Janković, M. (2014). Electricity Market in Serbia – Amandments to the Market Code. http://strategaeast.com/wp-content/uploads/2014/10/EMS_Metropol_17okt_2014.pdf. Accessed 28 May 2015.

  • Janković, M., Stojčevski, D., & Ignjatović, A. (2013). Practical Implementation of Balancing Responsibility Concept in the JP EMS Control Area. Serbia:CIGRE.

    Google Scholar 

  • Joy, J., Jasmin, E. A., & John, V. R. (2013). Challenges of smart grid, international journal of advanced research in electrical. Electronics and Instrumentation Engineering, 2(3), 976–981.

    Google Scholar 

  • Kaplan, R., & Norton, D. (1996). The Balanced Scorecard: Translating Strategy into Action. Boston:Harvard Business School Press.

    Google Scholar 

  • Kimball, R., Ross, M., Thornthwaite, W. (1998). The Data Warehouse Lifecycle Toolkit. Wiley New York

  • Kimball, R., Ross, M., Thornthwaite, W., Mundy, J. Becker, B. (2008). The data warehouse lifecycle toolkit, Second Edition. Wiley, Indianapolis.

  • Koliba, C., DeMenno, M., Brune, N., & Zia, A. (2014). The salience and complexity of building, regulating, and governing the smart grid: Lessons from a statewide public-private partnership. Energy Policy, 74, 243–252.

    Article  Google Scholar 

  • Leeds, D. J. (2009). The Smart Grid in 2010: Market Segments. GTM Research:Applications and Industry Players.

    Google Scholar 

  • Li, Y., Wang, L., Ji, L., Liao, C., (2013). A Data Warehouse Architecture supporting Energy Management of Intelligent Electricity System. in: Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering. (pp. 696–699) Atlantis Press, Paris, France.

  • Lukić, J. (2014). Approach to Multidimensional Data Modeling in BI Technology. in: ICIST 2014, 4th International Conference on Information Society and Technology. Kopaonik, Serbia.

  • Lund, P. D. (2014). How fast can businesses in the new energy sector grow? An analysis of critical factors. Renewable Energy, 66, 33–40.

    Article  Google Scholar 

  • Masayna, V., Koronios, A., Gao, J., Gendron, M. (2007). Data Quality And KPIs: A Link To Be Established (Research-in-Progress). in: Proceedings of the 2nd World Congress on Engineering Asset Management (EAM) and The 4th International Conference on Condition Monitoring, (pp. 1377–1386), ISBN 9781901892222.

  • Mejía-Lavalle, M., Argotte-Ramos, L. P. (2009). Business Intelligence applied to Electric Power Markets. In: WMLDM 2009, 1st Workshop on Machine Learning and Data Mining. Guanajuato, Mexico: Sociedad Mexicana de Inteligencia Artificial.

  • Min, C., Shiwen, M., & Yunhao, L. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171–209.

    Article  Google Scholar 

  • McLoughlin, F., Duffy, A., & Conlon, M. (2015). Clustering approach to domestic electricity load profile characterisation using smart metering data. Applied Energy, 141, 190–199.

    Article  Google Scholar 

  • Musil, J. (2012). ASAP Methodology Roadmaps and Phases. http://scn.sap.com/docs/DOC-8032. Accessed 28 May 2015.

  • National Institute for Standards and Technology NIST (2012):. NIST Framework and Roadmap for Smart Grid Interoperability Standards, Release 2.0, Washington, DC: U.S. Department of Commerce. U.S. Department of Commerce. http://www.nist.gov/smartgrid/upload/NIST_Framework_Release_2-0_corr.pdf. Accessed 28 May 2015.

  • Nechifora, A., Albub, M., Hairc, R., & Terzija, V. (2015). A flexible platform for synchronized measurements, data aggregation and information retrieval. Electric Power Systems Research, 120, 20–31.

    Article  Google Scholar 

  • PE EMS (2014). Market Code, available at: http://www.ems.rs/media/uploads/2014/12/Market-Code_26.11.2014-Revised-text.pdf. Accessed August 19, 2015.

  • Personal, E., Guerrero, J. E., Garcia, A., Pena, M., & Leon, C. (2014). Key performance indicators: A useful tool to assess Smart Grid goals. Energy, 76, 976–988.

    Article  Google Scholar 

  • Peters, M., Ketter, W., Saar-Tsechansky, M., & Collins, J. (2013). A reinforcement learning approach to autonomous decision-making in smart electricity markets. Machine Learning, 92(1), 5–39.

    Article  Google Scholar 

  • Pfenninger, S., Hawkes, A., & Keirstead, J. (2014). Energy systems modeling for twenty-first century energy challenges. Renewable and Sustainable Energy Reviews, 33, 74–86.

    Article  Google Scholar 

  • Popovic, T., Kezunovic, M., & Krstajic, B. (2015a). Smart grid data analytics for digital protective relay event recordings. Information Systems Frontiers., 17(3), 591–600.

    Article  Google Scholar 

  • Popovic, T., Kezunovic, M., & Krstajic, B. (2015b). Implementation requirements for automated fault data analytics in power systems. International Transactions on Electrical Energy Systems, 25(4), 731–752.

    Article  Google Scholar 

  • Rahimi, F., & Ipakchi, A. (2010). Demand response as a market resource under the smart grid paradigm. IEEE Transaction on Smart Grid, 1(1), 82–88.

    Article  Google Scholar 

  • (2015) Rules for allocation of available cross-border transfer capacities on borders of JP EMS control area from 01/01/2015 till 31/12/2015: PE “Elektromreža Srbije". http://www.ems.rs/media/uploads/2014/12/Rules-for-allocation-of-cross-border-transfer-capacities-for-2015-Split-auctions.pdf. Accessed 28 May 2015.

  • Sancho, J., Sánchez-Soriano, J., Chazarra, J. A., & Aparicio, J. (2008). Design and implementation of a decision support system for competitive electricity markets. Decision Support Systems, 44(4), 765–784.

    Article  Google Scholar 

  • SAP AG and SAP America, Inc. (2006): Multi-Dimensional Modeling with BI - A background to the techniques used to create BI InfoCubes. http://www.sdn.sap.com/irj/scn/go/portal/prtroot/docs/library/uuid/6ce7b0a4-0b01-0010-52ac-a6e813c35a84?overridelayout=true. Accessed 28 May 2015.

  • SAP AG. (2013): Dashboards and Presentation Design User Guide. http://help.sap.com/businessobject/product_guides/sbo41/en/sbo41sp2_dashd_user_en.pdf. Accesed August 19 2015.

  • SAP AG. (2014): ASAP Methodology Roadmaps and Phases. http://scn.sap.com/docs/DOC-8032. Accesed 7 July 2015.

  • Sen, A., & Sinha, A. P. (2005). A comparison of data warehousing methodologies. Communication of the ACM, 48(2), 79–84.

    Article  Google Scholar 

  • Stefanović, N., & Stefanović, D. (2011). Supply chain performance measurement system based on scorecards and web portals. Computer Science and Information Systems, 8(1), 167–192.

    Article  Google Scholar 

  • Stefanović, N., Stefanović, D., & Radenković, B. (2009). Supply network modelling and simulation methodology. Simulation Modelling Practice and Theory, 17, 743–766.

    Article  Google Scholar 

  • Stefanović, N., Stefanović, D., Radenković, B. (2011). Integrated Supply Chain Intelligence through Collaborative Planning, Analytics and Monitoring. In: I. Mahdavi, S. Mohebbi, N. Cho, eds. Electronic Supply Network Coordination in Intelligent and Dynamic Environments: Modeling and Implementation, IGI Global.ch3. (pp. 43–92).

  • Sua, W., & Huang, A. Q. (2014). A game theoretic framework for a next-generation retail electricity market with high penetration of distributed residential electricity suppliers. Applied Energy, 119, 341–350.

    Article  Google Scholar 

  • Sueyoshi, T., & Tadiparthi, G. R. (2008). An agent-based decision support system for wholesale electricity market. Decision Support Systems, 44(2), 425–446.

    Article  Google Scholar 

  • Vardakas, J., Zorba, N., & Verikoukis, C. (2015). Performance evaluation of power demand scheduling scenarios in a smart grid environment. Applied Energy, 142, 164–178.

    Article  Google Scholar 

  • Venkat, P., Saadat, M. (2009). Smart Grid: Leveraging Intelligent Communications to Transform the Power Infrastructure, White Paper, Cisco Systems, Inc.

  • Xu, L. (2005). Data Modeling and Processing in Deregulated Power System. Ph.D. Washington State University - School of Electrical Engineering and Computer Science.

  • Werner, M., Hermansson, U. (2002). Integrated Utility Data Warehousing - A Prerequisite To Keep Up With Competition On Electricity Markets, in: IEEE Conference Publications, (CP488), (pp. 130–135).

  • Zhaolin, Z. (2012). Research of Business Intelligence and Data Warehouse Modeling Techniques, In T. Zhang (Ed.), Mechanical Engineering and Technology, AISC 125. (pp. 125–132) Springer-Verlag Berlin Heidelberg.

Download references

Acknowledgments

This work was supported by Ministry of Education, Science and Technological Development of Republic of Serbia, grant number 174031.

Author information

Authors and Affiliations

  1. Public Enterprise Elektromreža Srbije, Kneza Miloša 11, Belgrade, 11000, Serbia

    Jelena Lukić

  2. School of Computing, Union University, Knez Mihailova 6/VI, Belgrade, 11000, Serbia

    Miloš Radenković

  3. Faculty of Organizational Sciences, University of Belgrade, Jove Ilića 154, Belgrade, 11000, Serbia

    Marijana Despotović-Zrakić, Aleksandra Labus & Zorica Bogdanović

Authors
  1. Jelena Lukić
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Miloš Radenković
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marijana Despotović-Zrakić
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Aleksandra Labus
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zorica Bogdanović
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zorica Bogdanović.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lukić, J., Radenković, M., Despotović-Zrakić, M. et al. Supply chain intelligence for electricity markets: A smart grid perspective. Inf Syst Front 19, 91–107 (2017). https://doi.org/10.1007/s10796-015-9592-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10796-015-9592-z

Keywords

Search

Navigation