Simulation-as-a-Service for Energy Communities: A Scalable User-Facing Optimization Platform
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.