Anti-Crisis Solutions for Regional Energy Sector
DOI:
https://doi.org/10.17059/2015-3-15Keywords:
reliability of power supplies, demand-side management, energy efficiency, distributed generation, cost management, assetAbstract
The paper considers anti-crisis solutions for the electricity sector that fall into the category of strategic ones. Their primary purpose is to ensure the flexibility and adaptability of the system and prevent emergencies in the future. The authors explain the need for a holistic approach to taking anti-crisis decisions in power engineering and propose ways to improve the economic mechanism of cost reduction based upon international practice and placed in the Russian context. The benefits of demand-side management in ensuring the reliability of power supplies amid crisis are shown. The paper looks at various implementation modalities for demand- side management programmes and explores development prospects for distributed generation in Russia and stand-alone power supply options for manufacturing companies. Factors are assessed that affect the cost effectiveness of going off the grid. A general scheme of cost management aimed at reaching the strategic goals of the regional electricity sector is presented. The authors reveal possible applications and advantages of using predictive analytics for effective cost management. Ways of improving asset management are considered as well as the possibility of their employment in the Russian context. The key barriers to their implementations and ways of overcoming them are identified.References
Gitelman, L. D. & Ratnikov, B. Ye. (2006). Energeticheskiy biznes [Energy business]. Moscow: Delo Publ., 600.
Gitelman, L. D., Ratnikov, B. Ye. & Kozhevnikov, M. V. (2013). Upravlenie sprosom na energiyu v regione [Demand management on energy in a region]. Ekonomika regiona [Economy of region], 2, 71-78.
Cousins, J. T. (2010). Using time of use tariffs in industrial, commercial and residential applications effectively. Sandton: TLC Engineering Solutions, 15. Available at: http://www.tlc.co.za/white_papers/pdf/using_time_of_use_tariffs_in_industrial_commercial_and_residential_applications_effectively.pdf (date of access: 12.04.2015).
Gualtieri, M., Curran, R. & Kisker, H. The Forrester Wave™: Big Data Predictive Analytics Solutions, 43. Available at: https://www.forrester.com/The+Forrester+Wave+Big+Data+Predictive+Analytics+Solutions+Q2+2015/fulltext/-/E-res115697 (date of access: 14.04.2015).
Mitroshkina, V. & Abramov, S. (2011). Opyt ispolzovaniya resheniya SAS v kompaniyakh neftegazovoy otrasli [Experience of use of the SAS solutions in the oil and gas companies]. SAS Institut [SAS Institute], 16. Available at: https://www.sas.com/offices/europe/russia/software/ industries/neftegaz.pdf (date of access: 14.04.2015).
Halper, F. (2014). Predictive Analytics for Business Advantage. TDWI, 31. Available at: http://tdwi.org/research/2013/12/best-practices-report-predictive-analytics-for-business-advantage.aspx?tc=page0 (date of access: 14.04.2015).
Kotorov, R. (2009). Enhancing Decision-Making, Cost-Efficiency, and Profitability with Predictive Analytics. The Information Builders, 15. Available at: http://www.informationbuilders.com/about_us/whitepapers/ download_form/4575 (date of access: 14.04.2015).
Siegel, E. (2010). Seven Reasons You Need Predictive Analytics Today. Prediction Impact Inc., 16. Available at: http://www.predictiveanalyticsworld.com/signup-whitepaper.php (date of access: 14.04.2015).
White, D. (2010). Predictive Analytics: The Right Tool for Tough Times. An Aberdeen Group white paper. Aberdeen Group, 25. Available at: ftp://public.dhe.ibm.com/ software/data/sw-library/cognos/pdfs/analystreports/ar_predictive_analytics_the_right_tool _for_tough_times.pdf (date of access: 14.04.2015).
Parr-Rud, O. (2012). Drive Your Business with Predictive Analytics. SAS Institute, 10. Available at: http://www.sas.com/content/dam/SAS/en_us/doc/whitepaper2/drive-your-business-with-predictive-analytics-105620.pdf.
Taylor, J. (2015). Analytics Capability Landscape. Identifying the right analytic capabilities for success. Decision Management Solutions, 34. Available at: http://birtanalytics.actuate.com/download/register/the-analytics-capabilities-landscape-study.pdf?_ga=1.212929372.998497308.1428993237 (date of access: 14.04.2015).
Stefanovic, N. (2014). Proactive Supply Chain Performance Management with Predictive Analytics. The Scientific World Journal, 121-138. Available at: http://dx.doi.org/10.1155/2014/528917 (date of access: 14.04.2015).
Eckerson, W. (2014). Making Predictive Analytics Pervasive, Benchmark Report. TechTarget, SearchBusinessAnalytics, 30. Available at: http://docs.media.bitpipe.com/io_11x/io_116391/item_923163/051514_EB_Making%20Predictive%20Analytics%20Pervasive.pdf (date of access: 14.04.2015).
Schneider, J., Gaul, A., Neumann, C., Hografer, J., Wellbow, W., Schwan, M. & Schnettler, A. Asset management techniques. Electrical Power and Energy Systems, 8, Session 41, Paper 1, 1-11. Available at: http://pscc.ee.ethz.ch/uploads/tx_ethpublications/fp1000.pdf (date of access: 14.04.2015).
Tinham, B. (1999). Power to the people. Control and Instrumentation, 2, 33-47.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2015 Leonid Davidovich Gitelman, Boris Aleksandrovich Bokarev, T.B. Gavrilova, Mikhail Viktorovich Kozhevnikov
This work is licensed under a Creative Commons Attribution 4.0 International License.
