Skip to main content

Big Data Application for Security of Renewable Energy Resources

Abstract

Renewable Energy Resources (RES) play a critical role in electrical systems due to continuous demand increases. As a significant application of RES, modern electrical networks are very complex because of communication tools, smart meters, and real-time data processing. These smart tools and ongoing communication generate a high-speed tsunami of data that require novel methods for better performance and decision-making. Even though big data has become an important and useful method for facing the challenges of large volume data, it is a double-edged sword. It brings certain risks to the data as well as the ability of convenience to the network. As such, data involved in these electrical systems become major targets of attacks. As a result, cybersecurity becomes a critical issue. In this chapter, we first provide a brief introduction to using RES in power systems and big data. Then, different aspects of security are summarized in the same context while RES is specifically addressed. Next, we introduce big data and finally, a relation between big data and security aspect of using RES is discussed.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-38557-6_11
  • Chapter length: 18 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   169.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-38557-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   219.99
Price excludes VAT (USA)
Hardcover Book
USD   219.99
Price excludes VAT (USA)
Fig. 11.1
Fig. 11.2
Fig. 11.3
Fig. 11.4
Fig. 11.5
Fig. 11.6
Fig. 11.7
Fig. 11.8

References

  1. H.M. Ruzbahani, H. Karimipour, Optimal incentive-based demand response management of smart households, in 2018 IEEE/IAS 54th Industrial and Commercial Power Systems Technical Conference (I&CPS) (2018), pp. 1–7

    Google Scholar 

  2. A. Askarzadeh, Solving electrical power system problems by harmony search: a review. Artif. Intell. Rev. 47(2), 217–251 (2017)

    CrossRef  Google Scholar 

  3. M. Henderson, D. Novosel, M.L. Crow, Electric Power Grid Modernization Trends, Challenges, and Opportunities (2017). ieeeorg-stg.ieee.org

  4. A. Rahimnejad, H.M. Rouzbahani, H. Karimipour, Smart Households Demand Response Management with Micro Grid. preprint arXiv (2019). arxiv.org , pp. 1–5

  5. V. Dinavahi, H. Karimipour, Parallel relaxation-based joint dynamic state estimation of large-scale power systems. IET Gener. Transm. Distrib. 10(2), 452–459 (2016)

    CrossRef  Google Scholar 

  6. F. Kratima, F. Gherbi, F. Lakdja, Applications of cooperative game theory in power system allocation problems. Leonardo J. Sci., 12 (2013). 193.226.7.140

    Google Scholar 

  7. M.J. Estahbanati, Hybrid probabilistic-harmony search algorithm methodology in generation scheduling problem. J. Exp. Theor. Artif. Intell. 26(2), 283–296 (2014)

    CrossRef  Google Scholar 

  8. L. Abdallah, T. El-Shennawy, Reducing Carbon Dioxide Emissions from Electricity Sector Using Smart Electric Grid Applications (2013). hindawi.com

  9. H. Karimipour, V. Dinavahi, Extended Kalman filter-based parallel dynamic state estimation. IEEE Trans. Smart Grid 6(3), 1539–1549 (2015)

    CrossRef  Google Scholar 

  10. S. Mohammadi, H. Mirvaziri, M. Ghazizadeh-Ahsaee, H. Karimipour, Cyber intrusion detection by combined feature selection algorithm. J. Inf. Secur. Appl. 44, 80–88 (2019)

    Google Scholar 

  11. H. Karimipour, V. Dinavahi, On False Data Injection Attack Against Dynamic State Estimation on Smart Power Grids (2017). ieeexplore.ieee.org

    Google Scholar 

  12. H. Karimipour, V. Dinavahi, On false data injection attack against dynamic state estimation on smart power grids, in 2017 IEEE International Conference on Smart Energy Grid Engineering (SEGE) (2017), pp. 388–393

    Google Scholar 

  13. H. Karimipour, V. Dinavahi, Accelerated parallel WLS state estimation for large-scale power systems on GPU, in 2013 North American Power Symposium (NAPS) (2013), pp. 1–6

    Google Scholar 

  14. H. Karimipour, V. Dinavahi, On detailed synchronous generator modeling for massively parallel dynamic state estimation, in 2014 North American Power Symposium (NAPS) (2014), pp. 1–6

    Google Scholar 

  15. Y. Zhang, T. Huang, E.F. Bompard, Big data analytics in smart grids: a review. Energy Inform 1(1), 8 (2018)

    CrossRef  Google Scholar 

  16. A. Azmoodeh, A. Dehghantanha, K.-K.R. Choo, Big data and internet of things security and forensics: challenges and opportunities, in Handbook of Big Data and IoT Security, (Springer, Cham, 2019), pp. 1–4

    Google Scholar 

  17. Renewable Energy Statistics, 2018. /publications/2018/Jul/Renewable-Energy-Statistics-2018

    Google Scholar 

  18. Y. Yan, Y. Qian, H. Sharif, D. Tipper, A survey on smart grid communication infrastructures: motivations, requirements and challenges. IEEE Commun. Surv. Tutorials 15(1), 5–20 (2013)

    CrossRef  Google Scholar 

  19. M. Zahran, Smart Grid Technology, Vision, Management and Control. WSEAS Transactions on Systems (2013). researchgate.net

  20. S. Geris, H. Karimipour, A feature selection-based approach for joint cyber-attack detection and state estimation, in IEEE Int. Conf. on Smart Energy Grid Engineering (SEGE) (2019), pp. 1–5

    Google Scholar 

  21. M.R. Begli, F. Derakhshan, H. Karimipour, A layered intrusion detection system for critical infrastructure using machine learning, in IEEE Int. Conf. on Smart Energy Grid Engineering (SEGE) (2019), pp. 1–5

    Google Scholar 

  22. H. Karimipour, V. Dinavahi, Robust massively parallel dynamic state estimation of power systems against cyber-attack. IEEE Access 6, 2984–2995 (2018)

    CrossRef  Google Scholar 

  23. H. Karimipour, A. Dehghantanha, R.M.M. Parizi, K.-K.R.R. Choo, H. Leung, A deep and scalable unsupervised machine learning system for cyber-attack detection in large-scale smart grids. IEEE Access 7, 1–1 (2019)

    CrossRef  Google Scholar 

  24. H.H. Pajouh, R. Javidan, R. Khayami, A. Dehghantanha, K.-K.R. Choo, A two-layer dimension reduction and two-tier classification model for anomaly-based intrusion detection in IoT backbone networks. IEEE Trans. Emerg. Top. Comput. 7(2), 314–323 (2019)

    CrossRef  Google Scholar 

  25. A. Azmoodeh, A. Dehghantanha, M. Conti, K.K.R. Choo, Detecting crypto-ransomware in IoT networks based on energy consumption footprint. J. Ambient. Intell. Humaniz. Comput. 9(4), 1141–1152 (2018)

    CrossRef  Google Scholar 

  26. S. Clements, H. Kirkham, Cyber-Security Considerations for the Smart Grid (2010). ieeexplore.ieee.org

  27. F. Sabena, A. Dehghantanha, A.P. Seddon, A review of vulnerabilities in identity management using biometrics, in 2010 Second International Conference on Future Networks (2010), pp. 42–49

    Google Scholar 

  28. S. Sagiroglu, A. Ozbilen, I. Colak, Vulnerabilities and measures on smart grid application in renewable energy, in 2012 International Conference on Renewable Energy Research and Applications (ICRERA) (2012), pp. 1–4

    Google Scholar 

  29. D.R. McKinnel, T. Dargahi, A. Dehghantanha, K.-K.R. Choo, A systematic literature review and meta-analysis on artificial intelligence in penetration testing and vulnerability assessment. Comput. Electr. Eng. 75, 175–188 (2019)

    CrossRef  Google Scholar 

  30. A. Metke, R. Ekl, Security Technology for Smart Grid Networks (2010). ieeexplore.ieee.org

  31. A. Ozbilen, I. Colak, S. Sagiroglu, A Survey on SCADA/Distributed Control System Current Security Development and Studies (2010)

    Google Scholar 

  32. Guidelines for smart grid cyber security, Gaithersburg, MD (2010)

    Google Scholar 

  33. A. Azmoodeh, A. Dehghantanha, R.M. Parizi, H. Karimipour, E. Modiri, D.E. Newton, Fuzzy pattern tree for edge malware detection and categorization in IoT zero trust distributed computing view project naive-Bayesian-based model for interoperability among heterogeneous systems in intelligent buildings view project fuzzy pattern tree for. Art. J. Syst. Archit. (2019)

    Google Scholar 

  34. F. Daryabar, A. Dehghantanha, N. I. Udzir, S. bin Shamsuddin, Towards secure model for SCADA systems, in Proceedings Title: 2012 International Conference on Cyber Security, Cyber Warfare and Digital Forensic (CyberSec) (2012), pp. 60–64

    Google Scholar 

  35. T. Flick, J. Morehouse, Securing the Smart Grid: Next Generation Power Grid Security (2010)

    Google Scholar 

  36. J. Sakhnini, H. Karimipour, and A. Dehghantanha, Smart Grid Cyber Attacks Detection using Supervised Learning and Heuristic Feature Selection. arXiv Prepr. arXiv1907.03313 (2019)

    Google Scholar 

  37. A. Azmoodeh, A. Dehghantanha, K.-K.R. Choo, Robust malware detection for internet of (battlefield) things devices using deep Eigenspace learning. IEEE Trans. Sustain. Comput. 4(1), 88–95 (2019)

    CrossRef  Google Scholar 

  38. K. Shaerpour, A. Dehghantanha, R. Mahmod, Trends in android malware detection. J. Digit. Forensics Secur. Law (2013)

    Google Scholar 

  39. A. Shalaginov, S. Banin, et al., Machine Learning Aided Static Malware Analysis: A Survey and Tutorial (Springer, Berlin, 2018)

    Google Scholar 

  40. I.A. Saeed, A. Selamat, A.M.A. Abuagoub, A survey on malware and malware detection systems. Int. J. Comput. Appl. 67(16), 25–31 (2013)

    Google Scholar 

  41. X. Wang, P. Yi, Security framework for wireless communications in smart distribution grid. IEEE Trans. Smart Grid 2(4), 809–818 (2011)

    CrossRef  Google Scholar 

  42. M. Gunduz, R. Das, Analysis of cyber-attacks on smart grid applications, in 2018 International Conference on Artificial Intelligence and Data Processing (IDAP) (2018). ieeexplore.ieee.org

  43. P. Zikopoulos, C. Eaton, Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data (2011).

    Google Scholar 

  44. G. Escobedo, N. Jacome, G. Arroyo-Figueroa, Big data & analytics to support the renewable energy integration of smart grids—case study: power solar generation, in Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security, 2017, pp. 267–275

    Google Scholar 

  45. T. Zhu, S. Xiao, Q. Zhang, Y. Gu, P. Yi, Y. Li, Emergent technologies in big data sensing: a survey. Int. J. Distrib. Sens. Networks 2015, 1–13 (2015)

    Google Scholar 

  46. S. Sagiroglu, R. Terzi, Y. Canbay, I. Colak, Big data issues in smart grid systems, in 2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA) (2016), pp. 1007–1012

    Google Scholar 

  47. H. Daki, A. El Hannani, A. Aqqal, A. Haidine, A. Dahbi, Big data management in smart grid: concepts, requirements and implementation. J. Big Data 4(1), 13 (2017)

    CrossRef  Google Scholar 

  48. A. Paro, E. Fadigas, A Methodology for Biomass Cogeneration Plants Overall Energy Efficiency Calculation and Measurement—A Basis for Generators Real Time Efficiency Data Disclosure (2011). ieeexplore.ieee.org

  49. A. MacGillivray, H. Jeffrey, M. Winskel, I. Bryden, Innovation and cost reduction for marine renewable energy: a learning investment sensitivity analysis. Technol. Forecast. Soc. Change 87, 108–124 (2014)

    CrossRef  Google Scholar 

  50. R.J.K. Wood, A.S. Bahaj, S.R. Turnock, L. Wang, M. Evans, Tribological design constraints of marine renewable energy systems. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 368(1929), 4807–4827 (2010)

    CrossRef  Google Scholar 

  51. J. Kaldellis, Optimum autonomous wind–power system sizing for remote consumers, using long-term wind speed data. Appl. Energy 71(3), 215–233 (2002)

    CrossRef  Google Scholar 

  52. C. Kacfah Emani, N. Cullot, C. Nicolle, Understandable big data: a survey. Comput. Sci. Rev. 17, 70–81 (2015)

    MathSciNet  CrossRef  Google Scholar 

  53. M. Chen, S. Mao, Y. Liu, Big data: a survey. Mob. Networks Appl. 19(2), 171–209 (2014)

    CrossRef  Google Scholar 

  54. B. Fang et al., The Contributions of Cloud Technologies to Smart Grid (Elsevier, Amsterdam)

    Google Scholar 

  55. S. Watson, A. Dehghantanha, Digital forensics: the missing piece of the internet of things promise. Comput. Fraud Secur. 2016(6), 5–8 (2016)

    CrossRef  Google Scholar 

  56. A. Aminnezhad, A. Dehghantanha, A survey on privacy issues in digital forensics. Int. J. Cyber-Security Digit. Forensics 1(4), 311–323 (2012)

    Google Scholar 

  57. P. McDaniel, S. McLaughlin, Security and Privacy Challenges in the Smart Grid (2009). ieeexplore.ieee.org

  58. F. Li, B. Luo, P. Liu, Secure Information Aggregation for Smart Grids Using Homomorphic Encryption (2010). ieeexplore.ieee.org

  59. G. Kalogridis, C. Efthymiou, S. Z. Denic, T. A. Lewis, R. Cepeda, Privacy for smart meters: towards undetectable appliance load signatures, in 2010 First IEEE International Conference on Smart Grid Communications, 2010, pp. 232–237

    Google Scholar 

  60. V. Rastogi, S. Nath, Differentially Private Aggregation of Distributed Time-Series with Transformation and Encryption (2010). dl.acm.org

    Google Scholar 

  61. L. Xie, Y. Mo, B. Sinopoli, False Data Injection Attacks In Electricity Markets (2010). ieeexplore.ieee.org

  62. S. Ruj, A. Pal, Analyzing Cascading Failures in Smart Grids Under Random and Targeted Attacks (2014). ieeexplore.ieee.org

  63. Y. Yuan, Z. Li, K. Ren, Quantitative Analysis of Load Redistribution Attacks in Power Systems (2012). ieeexplore.ieee.org

  64. R. Tan, V. B. Krishna, et al., Impact of Integrity Attacks on Real-Time Pricing in Smart Grids (2013). dl.acm.org

  65. L. Jia, J. Kim, R. Thomas, L. Tong, Impact of Data Quality on Real-Time Locational Marginal Price (2013). ieeexplore.ieee.org

  66. M. Esmalifalak, G. Shi, Z. Han, L. Song, Bad data injection attack and defense in electricity market using game theory study. IEEE Trans. Smart Grid 4(1), 160–169 (2013)

    CrossRef  Google Scholar 

  67. G. Epiphaniou, M. Walshe, H. Al-Khateeb, M. Hammoudeh, V. Katos, A. Dehghantanha, Non-interactive zero knowledge proofs for the authentication of iot devices in reduced connectivity environments. Ad Hoc Networks 95, 101988 (2019)

    CrossRef  Google Scholar 

  68. A. Hamlyn, H. Cheung, T. Mander, L. Wang, C. Yang, and R. Cheung, Network security management and authentication of actions for smart grids operations, in 2007 IEEE Canada Electrical Power Conference, 2007, pp. 31–36

    Google Scholar 

  69. M. M. Fouda, Z. M. Fadlullah, N. Kato, R. Lu, X. Shen, Towards a light-weight message authentication mechanism tailored for Smart Grid communications, in 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (2011), pp. 1018–1023

    Google Scholar 

  70. R. Ranchal et al., Protection of identity information in cloud computing without trusted third party, in 2010 29th IEEE Symposium on Reliable Distributed Systems (2010), pp. 368–372

    Google Scholar 

  71. M. Ben-Or, A. Wigderson, A. Wigderson, Completeness theorems for non-cryptographic fault-tolerant distributed computation, in Proceedings of the Twentieth Annual ACM Symposium on Theory of Computing—STOC ’88 (1988), pp. 1–10

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hossein Mohammadi Rouzbahani .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Verify currency and authenticity via CrossMark

Cite this chapter

Mohammadi Rouzbahani, H., Karimipour, H., Srivastava, G. (2020). Big Data Application for Security of Renewable Energy Resources. In: Choo, KK., Dehghantanha, A. (eds) Handbook of Big Data Privacy. Springer, Cham. https://doi.org/10.1007/978-3-030-38557-6_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-38557-6_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-38556-9

  • Online ISBN: 978-3-030-38557-6

  • eBook Packages: Computer ScienceComputer Science (R0)