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Cited by 449Digital Transformation in IndustryEnergy, Economy, and Technology TrendsDigital Innovation in Industries

Digitization, Digital Twins, Blockchain, and Industry 4.0 as Elements of Management Process in Enterprises in the Energy Sector

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Borowski, Piotr·2021·Energies
DOI 10.3390/en14071885
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Abstract

In the 21st century, it is becoming increasingly clear that human activities and the activities of enterprises affect the environment. Therefore, it is important to learn about the methods in which companies minimize the negative effects of their activities. The article presents the steps taken and innovative actions carried out by enterprises in the energy sector. The article analyzes innovative activities undertaken and implemented by enterprises from the energy sector. The relationships between innovative strategies, including, inter alia, digitization, and Industry 4.0 solutions, in the development of companies and the achieved results concerning sustainable development and environmental impact. Digitization has far exceeded traditional productivity improvement ranges of 3–5% per year, with a clear cost improvement potential of well above 25%. Enterprises on a large scale make attempts to increase energy efficiency by implementing the state-of-the-art innovative technical and technological solutions, which increase reliability and durability (material and mechanical engineering). Digitization of energy companies allows them to reduce operating costs and increases efficiency. With digital advances, the useful life of an energy plant can be increased up to 30%. Advanced technologies, blockchain, and the use of intelligent networks enables the activation of prosumers in the electricity market. Reducing energy consumption in industry and at the same time increasing energy efficiency for which the European Union is fighting in the clean air package for all Europeans have a positive impact on environmental protection, sustainable development, and the implementation of the decarbonization program.

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  • In the manufacturing industry, Digital Twin technology is gradually being integrated into various levels of production, ranging from individual machine tools to entire production lines, workshops, and even full-scale factories.

    “In the manufacturing industry, Digital Twin technology is gradually being integrated into various levels of production, ranging from individual machine tools to entire production lines, workshops, and even full-scale factories [16].”

    From Analysis of Digital Twin Applications in Energy Efficiency: A Systematic Review (2025) · cites this paper as [16]

    Section 4.7, “Digital Twins in Energy” (page not provided), describes digital twins for individual machines and production lines and says enterprises use production-plant data to build them. It does not establish that digital twins are already being integrated across workshops or entire factories; it describes broader adoption alongside smart factories as a developing trend. (LLM, conf=0.86)

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Research works printed but not linked: 59

  1. Varela, I. Energy Is Essential, but Utilities? Digitalization: What Does It Mean for the Energy Sector? In Digital Marketplaces Unleashed; Springer: Berlin/Heidelberg, Germany, 2018; pp. 829-838.
  2. Sheveleva, A.; Tyaglov, S.; Khaiter, P. Digital Transformation Strategies of Oil and Gas Companies: Preparing for the Fourth Industrial Revolution. In Digital Strategies in a Global Market Palgrave Macmillan; Springer: Cham, Switzerland, 2021; pp. 157-171.
  3. Kueppers, M.; Pineda, S.N.P.; Metzger, M.; Huber, M.; Paulus, S.; Heger, H.J.; Niessen, S. Decarbonization pathways of worldwide energy systems-Definition and modeling of archetypes. Appl. Energy 2021, 285, 116438. [CrossRef] · DOI
  4. Borowski, P.F. Adaptation strategy on regulated markets of power companies in Poland. Energy Environ. 2019, 30, 3-26. [CrossRef] · DOI
  5. Borowski, P.F. New Technologies and Innovative Solutions in the Development Strategies of Energy Enterprises. Hightech Innov. J. 2020, 1, 39-58. [CrossRef] · DOI
  6. Hernes, M., Rot, A., Jelonek, D., Eds.; Springer: Cham, Switzerland, 2020; Volume 887. [CrossRef] · DOI
  7. Manfren, M.; Sibilla, M.; Tronchin, L. Energy Modelling and Analytics in the Built Environment-A Review of Their Role for Energy Transitions in the Construction Sector. Energies 2021, 14, 679. [CrossRef] · DOI
  8. Recalde, M.Y. Which aspects may prevent the development of energy service companies? The impact of barriers and country- specific conditions in different regions. In Energy Services Fundamentals and Financing; Academic Press: New York, NY, USA, 2021; pp. 293-315. [CrossRef] · DOI
  9. Silvestre, B.S.; Ţîrcă, D.M. Innovations for sustainable development: Moving toward a sustainable future. J. Clean. Prod. 2019, 208, 325-332. [CrossRef] · DOI
  10. Chang, C.P.; Wen, J.; Zheng, M.; Dong, M.; Hao, Y. Is higher government efficiency conducive to improving energy use efficiency? Evidence from OECD countries. Econ. Model. 2018, 72, 65-77. [CrossRef] · DOI
  11. Safarzadeh, S.; Rasti-Barzoki, M.; Hejazi, S.R. A review of optimal energy policy instruments on industrial energy efficiency programs, rebound effects, and government policies. Energy Policy 2020, 139, 111342. [CrossRef] · DOI
  12. Wojdalski, J.; Krajnik, M.; Borowski, P.F.; Dró żd ż, B.; Kupczyk, A. Energy and water efficiency in the gelatine production plant. AIMS Geosci. 2020, 6, 491-503. [CrossRef] · DOI
  13. Tvaronavičien ė, M.; Lisin, E.; Kindra, V. Power Market Formation for Clean Energy Production as the Prerequisite for the Country's Energy Security. Energies 2020, 13, 4930. [CrossRef] · DOI
  14. Borowski, P.F. Zonal and Nodal Models of Energy Market in European Union. Energies 2020, 13, 4182. [CrossRef] · DOI
  15. Stephenson, J.R.; Sovacool, B.K.; Inderberg, T.H.J. Energy cultures and national decarbonisation pathways. Renew. Sustain. Energy Rev. 2021, 137, 110592. [CrossRef] · DOI
  16. Seroka-Stolka, O.; Fijorek, K. Enhancing corporate sustainable development: Proactive environmental strategy, stakeholder pressure and the moderating effect of firm size. Bus. Strategy Environ. 2020, 29, 2338-2354. [CrossRef] · DOI
  17. Seroka-Stolka, O. The development of green logistics for implementation sustainable development strategy in companies. Procedia-Soc. Behav. Sci. 2014, 151, 302-309. [CrossRef] · DOI
  18. Winch, S. Cultural determinants of leadership in enterprises-research results. Cent. Eur. Manag. J. 2011, 19, 102-113.
  19. Charkha, P.G.; Jaju, S.B. Decision Support System for Supply Chain Performance Measurement: Case of Textile Industry. In New Paradigm of Industry 4.0; Patnaik, S., Ed.; Studies in Big Data; Springer: Cham, Switzerland, 2020; Volume 64. [CrossRef] · DOI
  20. Medic, N.; Anisic, Z.; Tasic, N.; Zivlak, N.; Lalic, B. Technology Adoption in the Industry 4.0 Era: Empirical Evidence from Manufacturing Companies. In Proceedings of the IFIP International Conference on Advances in Production Management Systems, Novi Sad, Serbia, 30 August-3 September 2020;
  21. Lalic, B., Majstorovic, V., Marjanovic, U., von Cieminski, G., Romero, D., Eds.; Springer: Cham, Switzerland, 2020; Volume 591. [CrossRef] · DOI
  22. Żelazi ński, T. Properties of Biocomposites from Rapeseed Meal, Fruit Pomace and Microcrystalline Cellulose Made by Press Pressing: Mechanical and Physicochemical Characteristics. Materials 2021, 14, 890. [CrossRef] [PubMed] · DOI
  23. Hernes, M.; Rot, A.; Jelonek, D. Towards Industry 4.0-Current Challenges in Information Systems; Springer: Cham, Switzerland, 2020; Volume 887.
  24. Kościelniak, H.; Łęgowik-Małolepsza, M.; Łęgowik-Świ ącik, S. The Application of Information Technologies in Consideration of Augmented Reality and Lean Management of Enterprises in the Light of Sustainable Development. Sustainability 2019, 11, 2157. [CrossRef] · DOI
  25. Sun, C.; Wang, Y.; McMurtrey, M.D.; Jerred, N.D.; Liou, F.; Li, J. Additive manufacturing for energy: A review. Appl. Energy 2021, 282, 116041. [CrossRef] · DOI
  26. Bai, H.; Miao, S.; Ran, X.; Ye, C. Optimal dispatch strategy of a virtual power plant containing battery switch stations in a unified electricity market. Energies 2015, 8, 2268. [CrossRef] · DOI
  27. Wang, H.; Riaz, S.; Mancarella, P. Integrated techno-economic modeling, flexibility analysis, and business case assessment of an urban virtual power plant with multi-market co-optimization. Appl. Energy 2020, 259, 114142. [CrossRef] · DOI
  28. Navarra, A.; Ross, A.; Toro, N.; Ayala, F.; Marín, T. Quantitative Methods to Support Data Acquisition Modernization within Copper Smelters. Processes 2020, 8, 1478. [CrossRef] · DOI
  29. Borowski, P.F. Innovative Processes in Managing an Enterprise from the Energy and Food Sector in the Era of Industry 4.0. Processes 2021, 9, 381. [CrossRef] · DOI
  30. Li, Y.; Dai, J.; Cui, L. The impact of digital technologies on economic and environmental performance in the context of industry 4.0: A moderated mediation model. Int. J. Prod. Econ 2020, 107777. [CrossRef] · DOI · Paper page
  31. Sima, V.; Gheorghe, I.G.; Subić, J.; Nancu, D. Influences of the industry 4.0 revolution on the human capital development and consumer behavior: A systematic review. Sustainability 2020, 12, 4035. [CrossRef] · DOI
  32. Ahmad, N.; Shamsuddin, A.; Aslinda, A.S. Industry 4.0 implications on human capital: A review. J. Stud. Manag. Plan. 2018, 4, 221-235.
  33. Chaudhuri, A. Transformation with trustworthy digital: Policy desiderata for businesses in post Covid-19 worlds. EDPACS 2021, 63, 1-8. [CrossRef] · DOI
  34. Mahdin, H.; Abawajy, J.; Yaacob, S.S. Energy Efficient Approach in RFID Network. In Proceedings of the IOP Conference Series: Materials Science and Engineering, Bali, Indonesia, 19-20 March 2016; IOP Publishing: Bristol, UK, 2016; Volume 160, p. 12090.
  35. Su, J.; Sheng, Z.; Leung, V.C.M.; Chen, Y. Energy Efficient Tag Identification Algorithms for RFID: Survey, Motivation And New Design. IEEE Wirel. Commun. 2019, 26, 118-124. [CrossRef] · DOI
  36. Küfeoglu, S.; Liu, G.; Anaya, K.; Pollitt, M. Digitalisation and New Business Models in Energy Sector; University of Cambridge: Cambridge, UK, 2019. [CrossRef] · DOI
  37. Andoni, M.; Robu, V.; Flynn, D.; Abram, S.; Geach, D.; Jenkins, D.; McCallum, P.; Peacock, A. Blockchain technology in the energy sector: A systematic review of challenges and opportunities. Renew. Sustain. Energy Rev. 2019, 100, 143-174. [CrossRef] · DOI · Paper page
  38. Benisi, N.Z.; Aminian, M.; Javadi, B. Blockchain-based decentralized storage networks: A survey. J. Netw. Comput. Appl. 2020, 102656. [CrossRef] · DOI
  39. Son, Y.-B.; Im, J.-H.; Kwon, H.-Y.; Jeon, S.-Y.; Lee, M.-K. Privacy-Preserving Peer-to-Peer Energy Trading in Blockchain-Enabled Smart Grids Using Functional Encryption. Energies 2020, 13, 1321. [CrossRef] · DOI
  40. Di Silvestre, M.L.; Gallo, P.; Guerrero, J.M.; Musca, R.; Sanseverino, E.R.; Sciumè, G.; Vásquez, J.C.; Zizzo, G. Blockchain for power systems: Current trends and future applications. Renew. Sustain. Energy Rev. 2020, 119, 109585. [CrossRef] · DOI
  41. Wang, S.; Taha, A.F.; Wang, J.; Kvaternik, K.; Hahn, A. Energy Crowdsourcing and Peer-to-Peer Energy Trading in Blockchain- Enabled Smart Grids. IEEE Trans. Syst. Man Cybern. Syst. 2019, 49, 1612-1623. [CrossRef] · DOI
  42. Heo, K.; Kong, J.; Oh, S.; Jung, J. Development of operator-oriented peer-to-peer energy trading model for integration into the existing distribution system. Int. J. Electr. Power Energy Syst. 2021, 125, 106488. [CrossRef] · DOI
  43. Tushar, W.; Yuen, C.; Saha, T.K.; Morstyn, T.; Chapman, A.C.; Alam, M.J.E.; Hanif, S.; Poor, H.V. Peer-to-peer energy systems for connected communities: A review of recent advances and emerging challenges. Appl. Energy 2021, 282, 116131. [CrossRef] · DOI
  44. Jiang, A.; Yuan, H.; Li, D. A two-stage optimization approach on the decisions for prosumers and consumers within a community in the Peer-to-peer energy sharing trading. Int. J. Electr. Power Energy Syst. 2021, 125, 106527. [CrossRef] · DOI
  45. Tushar, W.; Saha, T.K.; Yuen, C.; Azim, M.I.; Morstyn, T.; Poor, H.V.; Niyato, D.; Bean, R. A coalition formation game framework for peer-to-peer energy trading. Appl. Energy 2020, 261, 114436. [CrossRef] · DOI
  46. Lan, T.; Liu, X.; Wang, S.; Jermsittiparsert, K.; Alrashood, S.T.; Rezaei, M.; Al-Ghussain, L.; Mohamed, M.A. An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles' Charging Demand. Energies 2021, 14, 569. [CrossRef] · DOI
  47. Zhang, X.; Xu, Q.; Lu, J.; Xu, J. Conceptual characteristics and analysis of typical application scenarios of energy blockchain. In Journal of Physics: Conference Series; IOP Publishing: Bristol, UK, 2021; Volume 1738, p. 12113.
  48. Wang, Q.; Su, M. Integrating blockchain technology into the energy sector-from theory of blockchain to research and application of energy blockchain. Comput. Sci. Rev. 2020, 37, 100275. [CrossRef] · DOI
  49. Mrowiec, D.; Sołtysik, M. Examples of possible blockchain technology applications in the power engineering sector (In Polish). Zesz. Nauk. Inst. Gospod. Surowcami Miner. Pol. Akad. Nauk 2018, 103, 133-144.
  50. Teng, S.Y.; Touš, M.; Leong, W.D.; How, B.S.; Lam, H.L.; Máša, V. Recent advances on industrial data-driven energy savings: Digital twins and infrastructures. Renew. Sustain. Energy Rev. 2021, 135, 110208. [CrossRef] · DOI
  51. Bevilacqua, M.; Bottani, E.; Ciarapica, F.E.; Costantino, F.; Di Donato, L.; Ferraro, A.; Mazzuto, G.; Monteriù, A.; Nardini, G.; Ortenzi, M.; et al. Digital twin reference model development to prevent operators' risk in process plants. Sustainability 2020, 12, 1088. [CrossRef] · DOI
  52. Di Nardo, M.; Madonna, M.; Murino, T.; Castagna, F. Modelling a Safety Management System Using System Dynamics at the Bhopal Incident. Appl. Sci. 2020, 10, 903. [CrossRef] · DOI
  53. Di Nardo, M.; Gallo, M.; Murino, M.; Santillo, L.C. System dynamics simulation for fire and explosion risk analysis in home environment. Int. Rev. Model. Simul. 2017, 10, 43-54. [CrossRef] · DOI
  54. Bylok, F.; Jelonek, D.; Tomski, P.; Wysłocka, E. Role of Social Capital in Creating Innovative Climate in Enterprises: The Case of Poland. In Proceedings of the ICICKM 2019 16th International Conference on Intellectual Capital Knowledge Management & Organisational Learning, Sydney, Australia, 5-6 December 2019; p. 73.
  55. Bratnicki, M.; Dyduch, W. Understanding Cognitive Biases in Strategic Decisions for Value Creation and Capture. In Contemporary Challenges in Cooperation and Coopetition in the Age of Industry 4.0; Zakrzewska-Bielawska, A., Staniec, I., Eds.; Springer Proceedings in Business and Economics; Springer: Cham, Switzerland, 2020. [CrossRef] · DOI
  56. Di Nardo, M. Developing a Conceptual Framework Model of Industry 4.0 for Industrial Management. Ind. Eng. Manag. Syst. 2020, 19, 551-560. [CrossRef] · DOI
  57. Żelazi ński, T.; Słoma, J.; Skudlarski, J.; Ekielski, A. The rape pomace and microcrystalline cellulose composites made by press processing. Sustainability 2020, 12, 1311. [CrossRef] · DOI
  58. Seow, Y.; Goffin, N.; Rahimifard, S.; Woolley, E. A 'Design for Energy Minimization'approach to reduce energy consumption during the manufacturing phase. Energy 2016, 109, 894-905. [CrossRef] · DOI
  59. Camarsa, G.; Toland, J.; Eldridge, J.; Potter, J.; Stephen Nottingham, S.; Marianne Geater, M.; de la Paz, C.; Martínez, E. Life and Circular Economy; Publications Office of the European Union: Luxembourg, 2017. [CrossRef] · DOI

Cited by

Sorted by citations

Authors
Borowski, P.
Affiliations
Warsaw University of Life Sciences (PL)
Corresponding author
Borowski, Piotr
ORCID
Borowski, Piotr
Venue
Energies
Publisher
MDPI AG
Published
2021-03-29
Volume
14
Issue
7
Pages
1885
ISSN
1996-1073 (ISSN-L)
DOI
10.3390/en14071885
OpenAlex
W3144115127
Cited by
449
References
2
Citations by year
2026: 48 · 2025: 91 · 2024: 107 · 2023: 102 · 2022: 69 · 2021: 32
FWCI
40.22
Type
journal article
Language
en
Open access
Licence
Locations
  • Energies (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
  • Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Energy, Economy, and Technology Trends
  • Social Sciences › Business, Management and Accounting › Management of Technology and Innovation › Digital Innovation in Industries
Keywords
digitization, digital twin, blockchain, Industry 4.0, energy companies, energy efficiency, sustainable development, environmental impact, prosumers, electricity market, decarbonization, operating costs

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