Abstract
In line with the Recovery and Resilience Plan, the New Generation Storage (NGS) project aims to establish a sustainable technological value chain. Its main objective is the sustainable manufacture and recovery of batteries for electric mobility. Thus, it was decided to evaluate three open-source optimization tools to identify their strengths and weaknesses, aiming to select the most robust optimization service for the project. In this sense, this paper presents an exploratory study on three open-source optimization tools. The main goal was to assess their performance in solving optimization problems that can be applied to real-world scenarios, like the NGS project. The evaluations were conducted using an N-queens benchmark, applying mixed-integer linear programming (MILP) modeling to validate the computational accuracy of each tool. Moreover, this study compares the performance, scalability, and computational efficiency of these tools to identify the most robust option for optimizing large-scale energy storage systems. The findings not only contribute to the selection of an optimal solver for the NGS project but also offer significant insights into the solver practical applicability in broader real-world scenarios, including industrial optimization and energy resource management.
This article is a result of the Innovation Pact “NGS - New Generation Storage” (reference 58), co-financed by NextGeneration EU, through the Incentive System “Agendas para a Inovação Empresarial” (“Agendas for Business Innovation”), within the Recovery and Resilience Plan (PRR).
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Acknowledgements
We wish to extend our genuine gratitude to DTx for their dedicated support, provision of invaluable resources, and constant encouragement throughout this endeavor. Their commitment to excellence and collaborative spirit have been instrumental in the success of this research. Additionally, we would like to express our appreciation to the PRR – NGS BP-15 agenda for providing the opportunity to disseminate our results and research.
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Alves, F., Petiz, M., Ribeiro, R., Abbasi, A., Carvalho, P.N., Rodrigues, R. (2024). An N-Queens Benchmark Using MILP Solvers Comparison Between Open-Source Optimization Tools. In: Pereira, A.I., et al. Optimization, Learning Algorithms and Applications. OL2A 2024. Communications in Computer and Information Science, vol 2281. Springer, Cham. https://doi.org/10.1007/978-3-031-77432-4_7
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