This work focuses on optimizing the scheduling of virtual power plants (VPPs)—as implemented in the Portuguese national project New Generation Storage (NGS)—to maximize social welfare and enhance energy trading efficiency within modern energy grids. By integrating distributed energy resources (DERs), including renewable energy sources and energy storage systems, VPPs represent a pivotal element of sustainable urban energy systems. The scheduling problem is formulated as a Mixed-Integer Linear Programming (MILP) task and addressed by using a parallelized simulated annealing (SA) algorithm implemented on high-performance computing (HPC) infrastructure. This parallelization accelerates solution space exploration, enabling the system to efficiently manage the complexity of larger DER networks and more sophisticated scheduling scenarios. The approach demonstrates its capability to align with the objectives of smart cities by ensuring adaptive and efficient energy distribution, integrating dynamic pricing mechanisms, and extending the operational lifespan of critical energy assets such as batteries. Rigorous simulations highlight the method’s ability to reduce optimization time, maintain solution quality, and scale efficiently, facilitating real-time decision making in energy markets. Moreover, the optimized coordination of DERs supports grid stability, enhances market responsiveness, and contributes to developing resilient, low-carbon urban environments. This study underscores the transformative role of computational infrastructure in addressing the challenges of modern energy systems, showcasing how advanced algorithms and HPC can enable scalable, adaptive, and sustainable energy optimization in smart cities. The findings demonstrate a pathway to achieving socially and environmentally responsible energy systems that align with the priorities of urban resilience and sustainable development.

This work focuses on optimizing the scheduling of Virtual Power Plant (VPP) to maximize social welfare and improve energy trading with the grid. Considering distributed energy resources (DERs) and battery lifespan limitations, we address the problem as a Mixed Integer Linear Programming (MILP) using a parallelized Simulated Annealing algorithm implemented on High-Performance Computing (HPC). This parallelization accelerates the exploration of the solution space, improving computational efficiency and enabling the system to handle larger networks of DERs and more complex scheduling decisions. The approach is evaluated through rigorous simulations, highlighting its effectiveness in reducing optimization time while maintaining high-quality solutions. The results demonstrate that our method significantly enhances the scalability of VPP scheduling and facilitates more responsive and efficient energy market participation, particularly in scenarios with stringent operational constraints and dynamic energy pricing. All experiments were conducted following a systematic pipeline, including hyperparameter optimization, to ensure that the configurations were optimized for efficient and effective performance.