Abstract
Digitalization is challenging in heavy industrial sectors, and many pilot projects facing difficulties to be replicated and scaled. Case studies are strong pedagogical vehicles for learning and sharing experience & knowledge, but rarely available in the literature. Therefore, this paper conducts a survey to gather a diverse set of nine industry cases, which are subsequently subjected to analysis using the business model canvas (BMC). The cases are summarized and compared based on nine BMC components, and a Value of Business Model (VBM) evaluation index is proposed to assess the business potential of industrial digital solutions. The results show that the main partners are industry stakeholders, IT companies and academic institutes. Their key activities for digital solutions include big-data analysis, machine learning algorithms, digital twins, and Internet of Things developments. The value propositions of most cases are improving energy efficiency and enabling energy flexibility. Moreover, the technology readiness levels of six industrial digital solutions are under level 7, indicating that they need further validation in real-world environments. Building upon these insights, this paper proposes six recommendations for future industrial digital solution development: fostering cross-sector collaboration, prioritizing comprehensive testing and validation, extending value propositions, enhancing product adaptability, providing user-friendly platforms, and adopting transparent recommendations.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Final energy consumption in industry - detailed statistics (2023). https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Final_energy_consumption_in_industry_-_detailed_statistics#:~:text=Since%201990%20electricity%20and%20natural,in%202021%20(%2D22.5%20%25)
Gao, D., Li, G., Yu, J.: Does digitization improve green total factor energy efficiency? Evidence from Chinese 213 cities. Energy 247, 123395 (2022)
Li, W., et al.: A novel operation approach for the energy efficiency improvement of the HVAC system in office spaces through real-time big data analytics. Renew. Sustain. Energy Rev. 127, 109885 (2020)
Howard, D.A., Ma, Z., Jørgensen, B.N.: Digital twin framework for energy efficient greenhouse industry 4.0. In: Novais, P., Vercelli, G., Larriba-Pey, J.L., Herrera, F., Chamoso, P. (eds.) ISAmI 2020. AISC, vol. 1239, pp. 293–297. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-58356-9_34
Howard, D.A., Ma, Z., Jørgensen, B.N.: Evaluation of industrial energy flexibility potential: a scoping review. In: 2021 22nd IEEE International Conference on Industrial Technology (ICIT). IEEE (2021)
Ma, Z., Jørgensen, B.N.: Energy flexibility of the commercial greenhouse growers: the potential and benefits of participating in the electricity market. In: 2018 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) (2018)
Ma, Z., et al.: An overview of digitalization for the building-to-grid ecosystem. Energy Inform. 4(2), 36 (2021)
Somu, N., MR, G.R., Ramamritham, K.: A deep learning framework for building energy consumption forecast. Renew. Sustain. Energy Rev. 137, 110591 (2021)
Khan, T., Yu, M., Waseem, M.: Review on recent optimization strategies for hybrid renewable energy system with hydrogen technologies: state of the art, trends and future directions. Int. J. Hydrogen Energy 47(60), 25155–25201 (2022)
Howard, D., et al.: Optimization of energy flexibility in cooling process for brewery fermentation with multi-agent simulation. In: 6th IEEJ International Workshop on Sensing, Actuation, Motion Control, and Optimization. Shibaura Institute of Technology, Tokyo, Japan (2020)
Subhan, S., et al.: Studies on the selection of a catalyst–oxidant system for the energy-efficient desulfurization and denitrogenation of fuel oil at mild operating conditions. Energy Fuels 33(9), 8423–8439 (2019)
Dey, A., Hoffman, D., Yodo, N.: Optimizing multiple process parameters in fused deposition modeling with particle swarm optimization. Int. J. Interact. Design Manuf. (IJIDeM) 14, 393–405 (2020)
Himeur, Y., et al.: A survey of recommender systems for energy efficiency in buildings: principles, challenges and prospects. Inf. Fusion 72, 1–21 (2021)
Ma, Z., Asmussen, A., Jørgensen, B.N.: Industrial consumers’ acceptance to the smart grid solutions: case studies from Denmark. In: Smart Grid Technologies - Asia (ISGT ASIA), 2015. IEEE Innovative (2015)
Ma, Z., Billanes, J.D., Jørgensen, B.N.: Aggregation potentials for buildings—business models of demand response and virtual power plants. Energies 10(10), 1646 (2017)
Ma, Z., Christensen, K., Jorgensen, B.N.: Business ecosystem architecture development: a case study of Electric Vehicle home charging. Energy Inform. 4, 37 (2021)
Osterwalder, A., Pigneur, Y.: Business model generation: a handbook for visionaries, game changers, and challengers, vol. 1. John Wiley & Sons (2020)
Díaz-Díaz, R., Muñoz, L., Pérez-González, D.: The business model evaluation tool for smart cities: application to SmartSantander use cases. Energies 10(3), 262 (2017)
Jin, H., et al.: Optimization and analysis of bioenergy production using machine learning modeling: Multi-layer perceptron, Gaussian processes regression, K-nearest neighbors, and Artificial neural network models. Energy Rep. 8, 13979–13996 (2022)
Howard, D.A., et al.: Energy flexibility potential in the brewery sector: a multi-agent based simulation of 239 Danish Breweries. In: 2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC). IEEE (2022)
Milczarski, P., et al.: Machine learning methods in energy consumption optimization assessment in food processing industry. In: 2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). IEEE (2021)
Brown, D., Kivimaa, P., Sorrell, S.: An energy leap? Business model innovation and intermediation in the ‘Energiesprong’retrofit initiative. Energy Res. Soc. Sci. 58, 101253 (2019)
Loock, M.: Unlocking the value of digitalization for the European energy transition: a typology of innovative business models. Energy Res. Soc. Sci. 69, 101740 (2020)
Maffei, A., Grahn, S., Nuur, C.: Characterization of the impact of digitalization on the adoption of sustainable business models in manufacturing. Procedia Cirp 81, 765–770 (2019)
Hiteva, R., Foxon, T.J.: Beware the value gap: creating value for users and for the system through innovation in digital energy services business models. Technol. Forecast. Soc. Chang. 166, 120525 (2021)
Héder, M.: From NASA to EU: the evolution of the TRL scale in public sector innovation. Innov. J. 22(2), 1–23 (2017)
Technology readiness levels (TRL); Extract from Part 19 - Commission Decision C(2014)4995 (2014). ec.europa.eu
https://www.energyinformatics.academy/projects-iea-iets-xviii-sub-task3
Jäger-Waldau, A., et al.: How photovoltaics can contribute to GHG emission reductions of 55% in the EU by 2030. Renew. Sustain. Energy Rev. 126, 109836 (2020)
Ma, Z., Jørgensen, B.N.: A discussion of building automation and stakeholder engagement for the readiness of energy flexible buildings. Energy Inform. 1(1), 54 (2018)
Christensen, K., Ma, Z., Jørgensen, B.N.: Technical, economic, social and regulatory feasibility evaluation of dynamic distribution tariff designs. Energies 14(10), 2860 (2021)
Christensen, K., et al.: Agent-based modeling of climate and electricity market impact on commercial greenhouse growers’ demand response adoption. In: 2020 RIVF International Conference on Computing and Communication Technologies (RIVF). Ho Chi Minh City, Vietnam: IEEE (2020)
Ma, Z.: The importance of systematical analysis and evaluation methods for energy business ecosystems. Energy Inform. 5(1), 1–6 (2022). https://doi.org/10.1186/s42162-022-00188-6
Tanev, S., Thomsen, M.S., Ma, Z.: Value co-creation: from an emerging paradigm to the next practices of innovation. In: ISPIM Innovation Symposium 2010. Quebec City, Canada: ISPIM (2010)
Grace Ma, Z., Asmussen, A., Jørgensen, B.N.: Influential factors to the industrial consumers’ smart grid adoption. In: International Energy Conference (ASTECHNOVA 2016). Yogyakart, Indonesia: IEEE (2016)
Acknowledgements
This paper is part of the IEA IETS Task XVIII: Digitalization, Artificial Intelligence and Related Technologies for Energy Efficiency and GHG Emissions Reduction in Industry, funded by the Danish funding agency, the Danish Energy Technology Development and Demonstration (EUPD) program, Denmark (Case no.134–21010), and the project “Data-driven best-practice for energy-efficient operation of industrial processes - A system integration approach to reduce the CO2 emissions of industrial processes” funded by EUDP (Case no.64020–2108).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 Crown
About this paper
Cite this paper
Ma, Z., Jørgensen, B.N., Levesque, M., Amazouz, M., Ma, Z. (2024). Business Models for Digitalization Enabled Energy Efficiency and Flexibility in Industry: A Survey with Nine Case Studies. In: Jørgensen, B.N., da Silva, L.C.P., Ma, Z. (eds) Energy Informatics. EI.A 2023. Lecture Notes in Computer Science, vol 14467. Springer, Cham. https://doi.org/10.1007/978-3-031-48649-4_15
Download citation
DOI: https://doi.org/10.1007/978-3-031-48649-4_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-48648-7
Online ISBN: 978-3-031-48649-4
eBook Packages: Computer ScienceComputer Science (R0)