Top

Published in:

2023 | OriginalPaper | Chapter

2. Data Analytics Applications in Digital Energy System Operation

Authors : Ali Paeizi, Mohammad Taghi Ameli, Sasan Azad

Published in: Energy Systems Transition

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

In today’s energy industry, the use of data analytics in modern digital energy system operation is an important research and innovation area. Data analysis has a key role in any modern industries and is a significant part of the optimal modern operation and planning in different industries, especially in smart power systems. This is a motivation for efficient data monitoring and processing methods to operate the digital energy system. This chapter aims to present algorithms and tools in the area of data analysis as well as the application of these tools to solve problems and challenges in modern electric power systems. In addition, basic concepts in data analysis methods, technical approaches, and research opportunities for analyzing energy data and its application in digital electric energy systems operation are discussed. Moreover, data security challenges, data management, and visualization with analysis of system input data are introduced.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Energy Systems Decarbonization: Design Optimization of a Commercial Building MG System Considering High Penetration of Renewable Energies

next chapter A New Stable Solar System for Electricity, Cooling, Heating, and Potable Water Production in Sunny Coastal Areas

Raoufat ME, Tomsovic K, Djouadi SM (2016) Virtual actuators for wide-area damping control of power systems. IEEE Trans Power Syst 31(6):4703–4711CrossRef

Usman MU, Omar Faruque M (2019) Applications of synchrophasor technologies in power systems. J Modern Power Syst Clean Energy 7(2):211–226CrossRef

Asad Z, Rehman Chaudhry MA (2017) A two-way street: Green big data processing for a greener smart grid. IEEE Syst J 11(2):784–795CrossRef

Marjani M et al (2017) Big IoT data analytics: architecture, opportunities, and open research challenges. IEEE Access 5:5247–5261CrossRef

Huang C et al (2021) Smart meter pinging and reading through AMI two-way communication networks to monitor grid edge devices and DERs. IEEE Trans Smart Grid PP(99):1

Portelinha JFM, Oliveira DQ (2018) The role of Big Data in smart grid communications. In: Big Data analytics in future power systems. CRC Press, pp 37–53CrossRef

Katz JS (2018) The role of Big Data and analytics in utility innovation. In: Big Data application in power systems. Elsevier, pp 43–56CrossRef

Stimmel CL (2018) Emerging security and data privacy challenges for utilities: case studies and solutions. In: Big Data application in power systems. Elsevier, pp 29–42CrossRef

Bessa RJ (2018) Future trends for big data application in power systems. In: Big data application in power systems. Elsevier, pp 223–242CrossRef

10.

Hu J, Vasilakos AV (2016) Energy big data analytics and security: challenges and opportunities. IEEE Trans Smart Grid 7(5):2423–2436CrossRef

11.

Sharifzadeh A, Ameli MT, Azad S (2021) Power system challenges and issues. In: Nazari-Heris M, Asadi S, Mohammadi-Ivatloo B, Abdar M, Jebelli H, Sadat-Mohammadi M (eds) Application of machine learning and deep learning methods to power system problems. Power systems. Springer, Cham. https://doi.org/10.1007/978-3-030-77696-1_1CrossRef

12.

Panda DK, Das S (2021) Smart grid architecture model for control, optimization and data analytics of future power networks with more renewable energy. J Clean Prod 301:126877CrossRef

13.

Farhoumandi M, Zhou Q, Shahidehpour M (2021) A review of machine learning applications in IoT-integrated modern power systems. Electr J 34(1):106879CrossRef

14.

Kezunovic M et al (2020) Big data analytics for future electricity grids. Electr Power Syst Res 189:106788CrossRef

15.

Völker B et al (2021) Watt’s up at home? Smart meter data analytics from a consumer-centric perspective. Energies 14(3):719CrossRef

16.

Miraftabzadeh SM et al (2021) Advances in the application of machine learning techniques for power system analytics: a survey. Energies 14(16):4776CrossRef

17.

Li Y, Zhang M, Chen C (2022) A deep-learning intelligent system incorporating data augmentation for short-term voltage stability assessment of power systems. Appl Energy 308:118347CrossRef

18.

Wang Y et al (2018) Review of smart meter data analytics: applications, methodologies, and challenges. IEEE Trans Smart Grid 10(3):3125–3148MathSciNetCrossRef

19.

Musbah H et al (2022) Energy management using multi-criteria decision making and machine learning classification algorithms for intelligent system. Electr Power Syst Res 203:107645CrossRef

20.

Khalkho AM, Mohanta DK (2022) RBFNN assisted transient instability-based risk assessment of cyber-physical power system. Int J Electr Power Energy Syst 137:107787CrossRef

21.

Kumari A, Tanwar S (2020) Secure data analytics for smart grid systems in a sustainable smart city: challenges, solutions, and future directions. Sustain Comput Inform Syst 28:100427

22.

Shi L, Dai Q, Ni Y (2018) Cyber–physical interactions in power systems: a review of models, methods, and applications. Electr Power Syst Res 163:396–412CrossRef

23.

Tu C et al (2017) Big data issues in smart grid–A review. Renew Sust Energ Rev 79:1099–1107CrossRef

24.

Moharm K (2019) State of the art in big data applications in microgrid: a review. Adv Eng Inform 42:100945CrossRef

25.

Zhou D et al (2016) Distributed data analytics platform for wide-area synchrophasor measurement systems. IEEE Trans Smart Grid 7(5):2397–2405CrossRef

26.

Chen L et al (2020) Robustness of cyber-physical power systems in cascading failure: survival of interdependent clusters. Int J Electr Power Energy Syst 114:105374CrossRef

27.

Ghorbanian M, Dolatabadi SH, Siano P (2019a) Big data issues in smart grids: a survey. IEEE Syst J 13(4):4158–4168CrossRef

28.

Moghaddass R, Wang J (2018) A hierarchical framework for smart grid anomaly detection using large-scale smart meter data. IEEE Trans Smart Grid 9(6):5820–5830CrossRef

29.

Hu H, Wen Y, Chua TS, Li X (2014) Toward scalable systems for big data analytics: a technology tutorial. IEEE Access 2:652–687CrossRef

30.

Wang K, Xu C, Zhang Y, Guo S, Zomaya A (2019) Robust big data analytics for electricity price forecasting in the smart grid. IEEE Trans Big Data 5(1):34–45CrossRef

31.

Wu J, Ota K, Dong M, Li J, Wang H (2017) Big data analysis based security situational awareness for smart grid. IEEE Trans Big Data 4(3):408–417CrossRef

32.

Jiang H, Wang K, Wang Y, Gao M, Zhang Y (2016) Energy big data: A survey. IEEE Access 4:3844–3861CrossRef

33.

Ali MS, Vecchio M, Pincheira M, Dolui K, Antonelli F, Rehmani MH (2019) Applications of blockchains in the Internet of Things: a comprehensive survey. IEEE Commun Surv Tuts 21(2):1676–1717CrossRef

34.

Daki H, El Hannani A, Aqqal A, Haidine A, Dahbi A (2017) Big data management in smart grid: concepts, requirements, and implementation. J Big Data 4(1):1–19CrossRef

35.

Wang K et al (2017) Wireless big data computing in smart grid. IEEE Wireless Commun 24(2):58–64CrossRef

36.

He Y, Mendis GJ, Wei J (2017) Real-time detection of false data injection attacks in smart grid: a deep learning-based intelligent mechanism. IEEE Trans Smart Grid 8(5):2505–2516CrossRef

37.

Aman S, Simmhan Y, Prasanna VK (2015) Holistic measures for evaluating prediction models in smart grids. IEEE Trans Knowl Data Eng 27(2):475–488CrossRef

38.

Chin WL, Li W, Chen HH (2017) Energy big data security threats in IoT-based smart grid communications. IEEE Commun Mag 55(10):70–75CrossRef

39.

Zainab A et al (2021) Big data management in smart grids: technologies and challenges. IEEE Access 9:73046–73059CrossRef

40.

Ghorbanian M, Dolatabadi SH, Masjedi M, Siano P (2019b) Communication in smart grids: a comprehensive review on the existing and future communication and information infrastructures. IEEE Syst J PP(99):1–14

41.

Liao D, Sun G, Yang G, Chang V (2018) Energy-efficient virtual content distribution network provisioning in cloud-based data centers. Future Gener Comput Syst 83:347–357CrossRef

42.

Akhavan-Hejazi H, Mohsenian-Rad H (2018) Power systems big data analytics: an assessment of paradigm shift barriers and prospects. Energy Rep 4:91–100CrossRef

43.

Wang M, Chow JH (2021) 11 data quality and privacy enhancement. In: Advanced data analytics for power systems, p 261CrossRef

44.

Mishra DP, Ray P (2018) Fault detection, location and classification of a transmission line. Neural Comput & Applic 30(5):1377–1424CrossRef

45.

Cheng L, Tao Y (2019) A new generation of AI: a review and perspective on machine learning technologies applied to smart energy and electric power systems. Int J Energy Res 43(6):1928–1973CrossRef

46.

Chen K et al (2018) Learning-based data analytics: moving towards transparent power grids. CSEE J Power Energy Syst 4(1):67–82CrossRef

47.

Munshi AA, Mohamed A-RI, Yasser. (2017) Big data framework for analytics in smart grids. Electr Power Syst Res 151:369–380CrossRef

48.

Tanwar S, Parekh K, Evans R (2020) Blockchain-based electronic healthcare record system for healthcare 4.0 applications. J Inform Secur Appl 50:102407

49.

Singh AP et al (2019) Storage and analysis of Synchrophasor data for event detection in Indian Power System Using Hadoop Ecosystem. In: Data and communication networks. Springer, Singapore, pp 291–304CrossRef

50.

Guo Y et al (2018) Complex power system status monitoring and evaluation using Big Data platform and machine learning algorithms: a review and a case study. Complexity 2018:1

51.

Khan M et al (2014) Parallel detrended fluctuation analysis for fast event detection on massive PMU data. IEEE Trans Smart Grid 6(1):360–368CrossRef

52.

Chu L et al (2017) Massive streaming PMU data modelling and analytics in smart grid state evaluation based on multiple high-dimensional covariance test. IEEE Tran Big Data 4(1):55–64CrossRef

53.

Ruiguang M, Haiyan W, Quanming Z, Yuan L (2017) Technical research on the electric power big data platform of smart grid. In: MATEC web of conferences, vol 139. EDP Sciences, p 00217

54.

Bhattarai BP et al (2019) Big data analytics in smart grids: state‐of‐the‐art, challenges, opportunities, and future directions. IET Smart Grid 2(2):141–154MathSciNetCrossRef

55.

Liu Y, Lei W, Li J (2020) D-PMU based applications for emerging active distribution systems: a review. Electr Power Syst Res 179:106063CrossRef

56.

Cao Z, Lin J, Wan C, Song Y, Taylor G, Li M (2017) Hadoop-based framework for big data analysis of synchronized harmonics in active distribution network. IET Gener Transm Distrib 11(16):3930–3937CrossRef

57.

Mollaei N, Mousavi SH (2017) Application of a hadoop-based distributed system for offline processing of power quality disturbances. Int J Power Electron Drive Syst(IJPEDS) 8(2):695–704CrossRef

58.

Khodayar M, Kaynak O, Khodayar ME (2017) Rough deep neural architecture for short-term wind speed forecasting. IEEE Trans Indus Inform 13(6):2770–2779CrossRef

59.

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

60.

Madadi S et al (2018) Application of Big Data analysis to operation of smart power systems. In: Big Data in engineering applications. Springer, Singapore, pp 347–362CrossRef

61.

Chang L, Wang X, Mao M (2017) Forecast of schedulable capacity for thermostatically controlled loads with big data analysis. 2017 IEEE 8th international symposium on power electronics for distributed generation systems (PEDG), IEEE, pp 1–6

62.

Husák M et al (2018) Survey of attack projection, prediction, and forecasting in cyber security. IEEE Commun Surv Tutor 21(1):640–660CrossRef

63.

Fang X et al (2019) A deep learning framework for predicting cyber attacks rates. EURASIP J Inf Secur 2019(1):1–11MathSciNet

64.

Chadza T, Kyriakopoulos KG, Lambotharan S (2020) Analysis of hidden Markov model learning algorithms for the detection and prediction of multi-stage network attacks. Futur Gener Comput Syst 108:636–649CrossRef

65.

Zhang Y, Lin F, Wang K (2020) Robustness of short-term wind power forecasting against false data injection attacks. Energies 13(15):3780CrossRef

66.

Luo J, Hong T, Fang S-C (2018) Benchmarking robustness of load forecasting models under data integrity attacks. Int J Forecast 34(1):89–104CrossRef

67.

Doynikova E, Novikova E, Kotenko I (2020) Attacker behaviour forecasting using methods of intelligent data analysis: a comparative review and prospects. Information 11(3):168CrossRef

Title: Data Analytics Applications in Digital Energy System Operation
Authors: Ali Paeizi
Mohammad Taghi Ameli
Sasan Azad
Publisher: Springer International Publishing
Book: Energy Systems Transition
Print ISBN: 978-3-031-22185-9

Electronic ISBN: 978-3-031-22186-6

Copyright Year: 2023
DOI: https://doi.org/10.1007/978-3-031-22186-6_2

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"