IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i23p9003-d986757.html
   My bibliography  Save this article

A New Approach to Risk Management in the Power Industry Based on Systems Theory

Author

Listed:
  • Dariusz Gołȩbiewski

    (PZU S.A., Jana Pawla II 24 Ave., 00-133 Warsaw, Poland)

  • Tomasz Barszcz

    (Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland)

  • Wioletta Skrodzka

    (Faculty of Management, Czȩstochowa University of Technology, Armii Krajowej 19B, 42-200 Czȩstochowa, Poland)

  • Igor Wojnicki

    (Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland)

  • Andrzej Bielecki

    (Institute of Security and Computer Science, Faculty of Exact and Natural Sciences, Pedagogical University in Kraków, Podchora̧żych 2, 30-084 Kraków, Poland)

Abstract

Contemporary risk management is based on statistical analysis. Such an approach has a few crucial disadvantages. First of all, it has limited applicability to new technological solutions. In this paper, a new idea for risk evaluation and management is put forward. The proposed approach is based on the autonomous systems theory. The theoretical foundation of the proposed idea is described and its prospective applications are discussed. The proposed measures of risk are based on the idea of the controllability of the system—the greater the level of controllability, the lower the risk. Various aspects of controllability are analyzed—economic, technological, and industrial. For each aspect of controllability, the problem of defining adequate measures for the level of risk is discussed. The proposed approach allows the risk assessor to analyze the system deeply. As a consequence, the analyst can assess the risk based not only on a posteriori statistics but also on an analysis of the crucial properties of the system. This allows the investigator to predict a priori possibilities of critical events. The proposed methodology is applied to the power industry.

Suggested Citation

  • Dariusz Gołȩbiewski & Tomasz Barszcz & Wioletta Skrodzka & Igor Wojnicki & Andrzej Bielecki, 2022. "A New Approach to Risk Management in the Power Industry Based on Systems Theory," Energies, MDPI, vol. 15(23), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:9003-:d:986757
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/23/9003/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/23/9003/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tadeusiewicz, Ryszard & Ogiela, Lidia & Ogiela, Marek R., 2008. "The automatic understanding approach to systems analysis and design," International Journal of Information Management, Elsevier, vol. 28(1), pages 38-48.
    2. Rocchetta, Roberto & Patelli, Edoardo, 2020. "A post-contingency power flow emulator for generalized probabilistic risks assessment of power grids," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    3. Giot, Pierre & Laurent, Sebastien, 2004. "Modelling daily Value-at-Risk using realized volatility and ARCH type models," Journal of Empirical Finance, Elsevier, vol. 11(3), pages 379-398, June.
    4. Pierre Giot & Sébastien Laurent, 2003. "Value-at-risk for long and short trading positions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(6), pages 641-663.
    5. Tiwari, Aviral Kumar & Trabelsi, Nader & Alqahtani, Faisal & Raheem, Ibrahim D., 2020. "Systemic risk spillovers between crude oil and stock index returns of G7 economies: Conditional value-at-risk and marginal expected shortfall approaches," Energy Economics, Elsevier, vol. 86(C).
    6. Liu, Guangqiang & Wei, Yu & Chen, Yongfei & Yu, Jiang & Hu, Yang, 2018. "Forecasting the value-at-risk of Chinese stock market using the HARQ model and extreme value theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 288-297.
    7. Yi-Wen Chen & Chu-Bin Lin & Anthony H. Tu, 2020. "Regime-Switching Processes and Mean-Reverting Volatility Models in Value-at-Risk Estimation: Evidence from the Taiwan Stock Index," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 56(12), pages 2693-2710, September.
    8. Wang, Can & Yan, Chao & Li, Gengfeng & Liu, Shiyu & Bie, Zhaohong, 2020. "Risk assessment of integrated electricity and heat system with independent energy operators based on Stackelberg game," Energy, Elsevier, vol. 198(C).
    9. Mensi, Walid & Shahzad, Syed Jawad Hussain & Hammoudeh, Shawkat & Zeitun, Rami & Rehman, Mobeen Ur, 2017. "Diversification potential of Asian frontier, BRIC emerging and major developed stock markets: A wavelet-based value at risk approach," Emerging Markets Review, Elsevier, vol. 32(C), pages 130-147.
    10. Xun Lu & Kin Lai & Liang Liang, 2014. "Portfolio value-at-risk estimation in energy futures markets with time-varying copula-GARCH model," Annals of Operations Research, Springer, vol. 219(1), pages 333-357, August.
    11. Wen, Danyan & Wang, Gang-Jin & Ma, Chaoqun & Wang, Yudong, 2019. "Risk spillovers between oil and stock markets: A VAR for VaR analysis," Energy Economics, Elsevier, vol. 80(C), pages 524-535.
    12. Andrzej Bielecki & Sylwia Nieszporska, 2019. "Analysis of Healthcare Systems by Using Systemic Approach," Complexity, Hindawi, vol. 2019, pages 1-12, April.
    13. Bhattacharya, Anindya & Kojima, Satoshi, 2012. "Power sector investment risk and renewable energy: A Japanese case study using portfolio risk optimization method," Energy Policy, Elsevier, vol. 40(C), pages 69-80.
    14. Giot, Pierre & Laurent, Sebastien, 2003. "Market risk in commodity markets: a VaR approach," Energy Economics, Elsevier, vol. 25(5), pages 435-457, September.
    15. Terje Aven & Ortwin Renn, 2009. "On risk defined as an event where the outcome is uncertain," Journal of Risk Research, Taylor & Francis Journals, vol. 12(1), pages 1-11, January.
    16. Yousaf, Imran, 2021. "Risk transmission from the COVID-19 to metals and energy markets," Resources Policy, Elsevier, vol. 73(C).
    17. David Cabedo, J. & Moya, Ismael, 2003. "Estimating oil price 'Value at Risk' using the historical simulation approach," Energy Economics, Elsevier, vol. 25(3), pages 239-253, May.
    18. Zhao, Yunfei & Huang, Linan & Smidts, Carol & Zhu, Quanyan, 2020. "Finite-horizon semi-Markov game for time-sensitive attack response and probabilistic risk assessment in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    19. Yanine, Fernando & Sanchez-Squella, Antonio & Barrueto, Aldo & Tosso, Joshua & Cordova, Felisa M. & Rother, Hans C., 2018. "Reviewing homeostasis of sustainable energy systems: How reactive and predictive homeostasis can enable electric utilities to operate distributed generation as part of their power supply services," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2879-2892.
    20. Tim Krehbiel & Lee C. Adkins, 2005. "Price risk in the NYMEX energy complex: An extreme value approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 25(4), pages 309-337, April.
    21. Li, Xiafei & Wei, Yu, 2018. "The dependence and risk spillover between crude oil market and China stock market: New evidence from a variational mode decomposition-based copula method," Energy Economics, Elsevier, vol. 74(C), pages 565-581.
    22. Jesus Beyza & Jose M. Yusta, 2021. "Integrated Risk Assessment for Robustness Evaluation and Resilience Optimisation of Power Systems after Cascading Failures," Energies, MDPI, vol. 14(7), pages 1-18, April.
    23. Jamshid Gharajedaghi & Russell L. Ackoff, 1984. "Mechanisms, organisms and social systems," Strategic Management Journal, Wiley Blackwell, vol. 5(3), pages 289-300, July.
    24. Lange, Oskar, 1970. "Introduction to Economic Cybernetics," Elsevier Monographs, Elsevier, edition 1, number 9780080066523.
    25. Alkathery, Mohammed A. & Chaudhuri, Kausik & Nasir, Muhammad Ali, 2022. "Implications of clean energy, oil and emissions pricing for the GCC energy sector stock," Energy Economics, Elsevier, vol. 112(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Michal Pikus & Jarosław Wąs, 2023. "Using Deep Neural Network Methods for Forecasting Energy Productivity Based on Comparison of Simulation and DNN Results for Central Poland—Swietokrzyskie Voivodeship," Energies, MDPI, vol. 16(18), pages 1-15, September.
    2. Kristin Wode & Tom Strube & Eva Schischke & Markus Hadam & Sarah Pabst & Annedore Mittreiter, 2023. "Tool Chain for Deriving Consistent Storage Model Parameters for Optimization Models," Energies, MDPI, vol. 16(3), pages 1-22, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alkathery, Mohammed A. & Chaudhuri, Kausik & Nasir, Muhammad Ali, 2022. "Implications of clean energy, oil and emissions pricing for the GCC energy sector stock," Energy Economics, Elsevier, vol. 112(C).
    2. Halkos, George & Tsirivis, Apostolos, 2019. "Using Value-at-Risk for effective energy portfolio risk management," MPRA Paper 91674, University Library of Munich, Germany.
    3. Hung, Jui-Cheng & Lee, Ming-Chih & Liu, Hung-Chun, 2008. "Estimation of value-at-risk for energy commodities via fat-tailed GARCH models," Energy Economics, Elsevier, vol. 30(3), pages 1173-1191, May.
    4. de Araújo, André da Silva & Garcia, Maria Teresa Medeiros, 2013. "Risk contagion in the north-western and southern European stock markets," Journal of Economics and Business, Elsevier, vol. 69(C), pages 1-34.
    5. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2014. "Realized volatility models and alternative Value-at-Risk prediction strategies," Economic Modelling, Elsevier, vol. 40(C), pages 101-116.
    6. Degiannakis, Stavros & Potamia, Artemis, 2017. "Multiple-days-ahead value-at-risk and expected shortfall forecasting for stock indices, commodities and exchange rates: Inter-day versus intra-day data," International Review of Financial Analysis, Elsevier, vol. 49(C), pages 176-190.
    7. František Čech & Jozef Baruník, 2019. "Panel quantile regressions for estimating and predicting the value‐at‐risk of commodities," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(9), pages 1167-1189, September.
    8. Florian Ielpo & Benoît Sévi, 2014. "Forecasting the density of oil futures," Working Papers 2014-601, Department of Research, Ipag Business School.
    9. Sévi, Benoît, 2014. "Forecasting the volatility of crude oil futures using intraday data," European Journal of Operational Research, Elsevier, vol. 235(3), pages 643-659.
    10. Jung-Bin Su, 2014. "How to mitigate the impact of inappropriate distributional settings when the parametric value-at-risk approach is used," Quantitative Finance, Taylor & Francis Journals, vol. 14(2), pages 305-325, February.
    11. Yun-Shi Dai & Peng-Fei Dai & Wei-Xing Zhou, 2023. "Tail dependence structure and extreme risk spillover effects between the international agricultural futures and spot markets," Papers 2303.11030, arXiv.org.
    12. repec:ipg:wpaper:2014-053 is not listed on IDEAS
    13. Cheong, Chin Wen, 2009. "Modeling and forecasting crude oil markets using ARCH-type models," Energy Policy, Elsevier, vol. 37(6), pages 2346-2355, June.
    14. Dimitrios P. Louzis & Spyros Xanthopoulos‐Sisinis & Apostolos P. Refenes, 2013. "The Role of High‐Frequency Intra‐daily Data, Daily Range and Implied Volatility in Multi‐period Value‐at‐Risk Forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(6), pages 561-576, September.
    15. Youssef, Manel & Belkacem, Lotfi & Mokni, Khaled, 2015. "Value-at-Risk estimation of energy commodities: A long-memory GARCH–EVT approach," Energy Economics, Elsevier, vol. 51(C), pages 99-110.
    16. Halkos, George E. & Tsirivis, Apostolos S., 2019. "Value-at-risk methodologies for effective energy portfolio risk management," Economic Analysis and Policy, Elsevier, vol. 62(C), pages 197-212.
    17. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "Are realized volatility models good candidates for alternative Value at Risk prediction strategies?," MPRA Paper 30364, University Library of Munich, Germany.
    18. Stavros Degiannakis & Pamela Dent & Christos Floros, 2014. "A Monte Carlo Simulation Approach to Forecasting Multi-period Value-at-Risk and Expected Shortfall Using the FIGARCH-skT Specification," Manchester School, University of Manchester, vol. 82(1), pages 71-102, January.
    19. Kostas Andriosopoulos & Nikos Nomikos, 2012. "Risk management in the energy markets and Value-at-Risk modelling: a Hybrid approach," RSCAS Working Papers 2012/47, European University Institute.
    20. Grané, Aurea & Veiga, Helena, 2010. "Outliers in Garch models and the estimation of risk measures," DES - Working Papers. Statistics and Econometrics. WS ws100502, Universidad Carlos III de Madrid. Departamento de Estadística.
    21. Dilip Kumar & S. Maheswaran, 2013. "Return, Volatility and Risk Spillover from Oil Prices and the US Dollar Exchange Rate to the Indian Industrial Sectors," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 7(1), pages 61-91, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:9003-:d:986757. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.