Energy Management System for Renewable Energy and Electric Vehicle-Based Industries Using Digital Twins: A Waste Management Industry Case Study
Source abstract
Abstract
The integration of renewable energy sources, battery energy storage, and electric vehicles into industrial systems unlocks new opportunities for reducing emissions and improving sustainability. However, the coordination and management of these new technologies also pose new challenges due to complex interactions. This paper proposes a methodology for designing a holistic energy management system, based on advanced digital twins and optimization techniques, to minimize the cost of supplying industry loads and electric vehicles using local renewable energy sources, second-life battery energy storage systems, and grid power. The digital twins represent and forecast the principal energy assets, providing variables necessary for optimizers, such as photovoltaic generation, the state of charge and state of health of electric vehicles and stationary batteries, and industry power demand. Furthermore, a two-layer optimization framework based on mixed-integer linear programming is proposed. The optimization aims to minimize the cost of purchased energy from the grid, local second-life battery operation, and electric vehicle fleet charging. The paper details the mathematical fundamentals behind digital twins and optimizers. Finally, a real-world case study is used to demonstrate the operation of the proposed approach within the context of the waste collection and management industry. The study confirms the effectiveness of digital twins for forecasting and performance analysis in complex energy systems. Furthermore, the optimization strategies reduce the operational costs by 1.3%, compared to the actual industry procedure, resulting in daily savings of EUR 24.2 through the efficient scheduling of electric vehicle fleet charging.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Digital Transformation in Industry · Advanced Battery Technologies Research · Flexible and Reconfigurable Manufacturing Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Catalonia Institute for Energy Research (IREC), 08930 Sant Adrià del Besos, Spain
Centre d’Innovació Tecnològica en Convertidors Estàtics i Accionaments (CITCEA-UPC), Departament d’Enginyeria Elèctrica, Escola Tècnica Superior d’Enginyeria Industrial de Barcelona (ETSEIB), Universitat Politècnica de Catalunya, 08028 Barcelona, Spain
Funding
Project PLEC2021-008152
References (45)
Show source-provided references
1. IEA (2025, February 26). Renewables 2024. International Energy Agency, Paris, 2024. Available online: https://www.iea.org/reports/renewables-2024.
2. Optimization of the integration of photovoltaic systems on buildings for self-consumption–Case study in France10.1016/j.cacint.2021.100057 ↗
3. Integrated optimization of charging infrastructure, fleet size and vehicle operation in shared autonomous electric vehicle system considering vehicle-to-grid10.1016/j.renene.2024.120760 ↗
4. Digital Twin: Benefits, use cases, challenges, and opportunities10.1016/j.dajour.2023.100165 ↗
5. Digital twin of electric vehicle battery systems: Comprehensive review of the use cases, requirements, and platforms10.1016/j.rser.2023.113280 ↗
6. Deep reinforcement learning-based energy management strategy for fuel cell buses integrating future road information and cabin comfort control10.1016/j.enconman.2024.119032 ↗
7. Towards the future of smart electric vehicles: Digital twin technology10.1016/j.rser.2021.110801 ↗
8. Ibrahim, M., Rjabtšikov, V., and Gilbert, R. (2023). Overview of Digital Twin Platforms for EV Applications. Sensors, 23.10.3390/s23031414 ↗
9. A Digital Twin of Charging Stations for Fleets of Electric Vehicles10.1109/ACCESS.2023.3330833 ↗
10. Iyer, N.G., Ponnurangan, S., Abdul Gafoor, N.A., Rajendran, A., Lashab, A., Saha, D., and Guerrero, J.M. (2024). A Novel Heuristic Algorithm Integrating Battery Digital-Twin-Based State-of-Charge Estimation for Optimized Electric Vehicle Charging. Electronics, 13.10.3390/electronics13224412 ↗
11. Digital twin for battery systems: Cloud battery management system with online state-of-charge and state-of-health estimation10.1016/j.est.2020.101557 ↗
12. Digital twin of a MWh-scale grid battery system for efficiency and degradation analysis10.1016/j.apenergy.2023.120774 ↗
13. Energy digital twin technology for industrial energy management: Classification, challenges and future10.1016/j.rser.2022.112407 ↗
14. Bécue, A., Maia, E., Feeken, L., Borchers, P., and Praça, I. (2020). A New Concept of Digital Twin Supporting Optimization and Resilience of Factories of the Future. Appl. Sci., 10.10.3390/app10134482 ↗
15. A Digital Twin-Based Approach for Designing and Multi-Objective Optimization of Hollow Glass Production Line10.1109/ACCESS.2017.2766453 ↗
16. Florescu, A. (2024). Digital Twin for Flexible Manufacturing Systems and Optimization Through Simulation: A Case Study. Machines, 12.10.3390/machines12110785 ↗
17. Lee, D., Kim, C.K., Yang, J., Cho, K.Y., Choi, J., Noh, S.D., and Nam, S. (2022). Digital Twin-Based Analysis and Optimization for Design and Planning of Production Lines. Machines, 10.10.3390/machines10121147 ↗
18. Digital Twins for Microgrids: Opening a New Dimension in the Power System10.1109/MPE.2023.3324296 ↗
19. DeepTwin: A Deep Reinforcement Learning Supported Digital Twin Model for Micro-Grids10.1109/ACCESS.2024.3521124 ↗
20. Miao, L., Zhao, J., Ma, J., Wei, X., and Hu, Y. (2024, January 13–15). Study on Optimization and Scheduling Strategies of Distributed Energy Storage Based on Digital Twin. Proceedings of the 2024 4th International Conference on Smart Grid and Energy Internet (SGEI), Shenyang, China.10.1109/SGEI63936.2024.10914292 ↗
21. Yalçin, T., Paradell Solà, P., Stefanidou-Voziki, P., Domínguez-García, J.L., and Demirdelen, T. (2023). Exploiting Digitalization of Solar PV Plants Using Machine Learning: Digital Twin Concept for Operation. Energies, 16.10.3390/en16135044 ↗
22. Lasseter, R., Akhil, A., Marnay, C., Stephens, J., Dagle, J., Guttromsom, R., Meliopoulos, A., Yinger, R., and Eto, J. (2002). Integration of distributed energy resources. The CERTS Microgrid Concept, California Energy Commission.10.2172/799644 ↗
23. A computational performance comparison of MILP vs. MINLP formulations for oil production optimisation10.1016/j.compchemeng.2020.106903 ↗
24. Effects of binary variables in mixed integer linear programming based unit commitment in large-scale electricity markets10.1016/j.epsr.2018.03.019 ↗
25. Energy-Aware Flexible Job Shop Scheduling Using Mixed Integer Programming and Constraint Programming10.1155/2021/8035806 ↗
26. Bernabeu, A., Clemente, A., Díaz, F., and Trilla, L. (2024, January 3–6). Experimental data granularity studies for the development of NMC Li-ion battery models. Proceedings of the IECON 2024-50th Annual Conference of the IEEE Industrial Electronics Society, Chicago, IL, USA.10.1109/IECON55916.2024.10905178 ↗
27. Montes, T., Etxandi-Santolaya, M., Eichman, J., Ferreira, V.J., Trilla, L., and Corchero, C. (2022). Procedure for Assessing the Suitability of Battery Second Life Applications after EV First Life. Batteries, 8.10.3390/batteries8090122 ↗
28. Ji, C., Dai, J., Zhai, C., Wang, J., Tian, Y., and Sun, W. (2024). A Review on Lithium-Ion Battery Modeling from Mechanism-Based and Data-Driven Perspectives. Processes, 12.10.3390/pr12091871 ↗
29. Algorithms for Advanced Battery-Management Systems10.1109/MCS.2010.936293 ↗
30. A comprehensive study on battery electric modeling approaches based on machine learning10.1186/s42162-021-00171-7 ↗
31. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models10.1149/1945-7111/ab9050 ↗
32. Asymptotic Reduction of a Lithium-Ion Pouch Cell Model10.1137/20M1336898 ↗
33. Marquis, S.G. (2020). Long-Term Degradation of Lithium-Ion Batteries. [Ph.D. Thesis, University of Oxford].
34. Lithium-ion battery degradation: How to model it10.1039/D2CP00417H ↗
35. Optimizing Battery RUL Prediction of Lithium-Ion Batteries Based on Harris Hawk Optimization Approach Using Random Forest and LightGBM10.1109/ACCESS.2023.3304699 ↗
36. Python Battery Mathematical Modelling (PyBaMM)
37. Scikit-learn: Machine Learning in Python
38. Borrego-Orpinell, G., Forero, J.F., Caprara, A., and Díaz-González, F. (2025). Impact Assessment of Second-Life Batteries and Local Photovoltaics for Decarbonizing Enterprises Through System Digitalization and Energy Management. Energies, 18.10.3390/en18051198 ↗
39. Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an Indian pig iron manufacturing organization10.1016/j.energy.2016.10.068 ↗
40. Stroe, D.I., Swierczynski, M., Stroe, A.I., Teodorescu, R., Laerke, R., and Kjaer, P.C. (2015, January 20–24). Degradation behaviour of Lithium-ion batteries based on field measured frequency regulation mission profile. Proceedings of the 2015 IEEE Energy Conversion Congress and Exposition (ECCE), Montreal, QC, Canada.10.1109/ECCE.2015.7309663 ↗
41. Degradation-conscious charge management: Comparison of different techniques to include battery degradation in Electric Vehicle Charging Optimization10.1016/j.est.2024.111560 ↗
42. Trucks, R. (2025, March 23). D26 ZE LEC Spec Sheet 2022, 2022. Available online: https://www.renault-trucks.co.uk/sites/default/files/2022-05/D26%20ZE%20LEC%20Spec%20Sheet%202022.pdf.
43. (2025, January 15). Apache Airflow, Version 2.5.0. Apache Software Foundation, 2015. Available online: https://airflow.apache.org.
44. Evolution of aging mechanisms and performance degradation of lithium-ion battery from moderate to severe capacity loss scenarios10.1016/j.cej.2024.155588 ↗
45. Wei, Y., Yao, Y., Pang, K., Xu, C., Han, X., Lu, L., Li, Y., Qin, Y., Zheng, Y., and Wang, H. (2022). A Comprehensive Study of Degradation Characteristics and Mechanisms of Commercial Li(NiMnCo)O2 EV Batteries under Vehicle-To-Grid (V2G) Services. Batteries, 8.10.3390/batteries8100188 ↗