Energy community simulations based on optimization models are increasingly used to evaluate energy sharing strategies among prosumers. However, integrating such computationally intensive models into user-facing applications presents significant challenges, including limited solver concurrency, long execution times, and the need for scalable and secure infrastructures. This paper presents a scalable architecture for delivering energy community simulations as a service. The proposed system integrates optimization models defined in Pyomo and solved using Gurobi within a distributed environment based on remote compute servers. To address bounded solver concurrency and avoid blocking in the API layer under concurrent requests, a job-based execution model is introduced, enabling asynchronous task handling and controlled concurrency. The architecture is implemented using a containerized approach, combining an API layer for request handling with a distributed execution layer for optimization tasks, and infrastructure as code to ensure reproducibility and security. Experimental evaluation demonstrates the system's ability to manage concurrent simulation workloads while maintaining responsiveness and respecting solver limitations. The results highlight how appropriate system design enables the practical deployment of optimization-based energy simulations, bridging the gap between research models and real-world applications.
D. Pereira, Pedro Rafael, Jo˜ao Soares et al.· Annual International Compute...· 0 citations
The rapid transition toward sustainable electrified transportation has led to the urgent deployment of electric vehicle (EV) fleets in urban logistics, where limited battery capacity and the need for en-route charging introduce new challenges for EV routing optimization. The final route, travel time, and operational cost are directly affected by the inclusion of charging stations in the routing objective. Unlike conventional vehicle routing problems (VRPs), the electric VRP (EVRP) requires energy feasibility constraints to be tightly integrated with routing decisions. In this study, we introduce a scalable, simulation-based EVRP framework that explicitly incorporates charging-station insertion into a metaheuristic optimization solver. A particle swarm optimization (PSO) approach is adopted to address the combinatorial complexity of large-scale instances, while feasibility is enforced through state-of-charge tracking and adaptive charging decisions. The proposed framework is evaluated and compared with a heuristic baseline algorithm (BR-TSP) under multiple simulation scenarios. Results demonstrate that the proposed approach is fully feasible across all tested scenarios, significantly outperforms classical exact solvers in scalability, and highlights the critical role of explicit charging integration in realistic EV fleet operations. The findings reveal practical strategies for energy-aware routing in large-scale electric mobility applications.
Khaled Bouhadef, José Almeida, Jo˜ao Soares et al.· Proceedings of the Genetic a...· 0 citations
