Highlights The integration of advanced computational techniques into energy optimization is crucial to building sustainable and efficient urban energy systems. VPPs play a key role in smart cities by enabling the seamless management of renewable energy resources, energy storage, and electric vehicles. This study demonstrates how HPC and parallelized SA can optimize energy distribution, improve grid stability, and maximize social welfare. By addressing challenges like battery lifespan and real-time energy trading, this work supports the development of smarter, more sustainable cities with resilient and equitable energy systems. What are the main findings? A novel parallelized SA algorithm was developed and implemented by using OpenMP on HPC infrastructure, delivering a substantial reduction in computational time for VPP scheduling. The approach showcases significant scalability, efficiently optimizing large-scale VPP networks with up to 512 prosumers, ensuring that the system remains robust as network complexity and size increase. By achieving near-linear speedup ratios across up to 32 cores, the algorithm leverages parallel computing to accelerate decision-making processes in real-time energy markets. The study integrates critical operational constraints, such as battery lifespan limitations and dynamic energy pricing, enabling sustainable and adaptive energy management while maintaining high-quality optimization outcomes. Extensive simulations demonstrate the effectiveness of the proposed framework in balancing energy acquisition, storage, and dispatch decisions across diverse DERs, contributing to smarter energy distribution systems. What is the implication of the main findings? The enhanced computational efficiency and scalability of the parallelized SA approach make it a practical and advanced solution for real-time VPP scheduling, ensuring reliable and adaptive energy management in dynamic market environments. This framework supports the development of future smart city energy systems-as evidenced by its strategic deployment in Portugal in the national project New Generation Storage (NGS)-by enabling the seamless integration of renewable energy sources, electric vehicles, and energy storage systems, ultimately contributing to a resilient, low-carbon energy ecosystem. By optimizing energy distribution with a focus on maximizing social welfare, the framework aligns with the principles of economic sustainability, grid stability, and environmental responsibility in smart cities. The findings highlight the transformative role of HPC and parallel computing in addressing the growing complexity of energy systems, providing a scalable and efficient blueprint for enhancing energy optimization and distribution in urban settings.Highlights The integration of advanced computational techniques into energy optimization is crucial to building sustainable and efficient urban energy systems. VPPs play a key role in smart cities by enabling the seamless management of renewable energy resources, energy storage, and electric vehicles. This study demonstrates how HPC and parallelized SA can optimize energy distribution, improve grid stability, and maximize social welfare. By addressing challenges like battery lifespan and real-time energy trading, this work supports the development of smarter, more sustainable cities with resilient and equitable energy systems. What are the main findings? A novel parallelized SA algorithm was developed and implemented by using OpenMP on HPC infrastructure, delivering a substantial reduction in computational time for VPP scheduling. The approach showcases significant scalability, efficiently optimizing large-scale VPP networks with up to 512 prosumers, ensuring that the system remains robust as network complexity and size increase. By achieving near-linear speedup ratios across up to 32 cores, the algorithm leverages parallel computing to accelerate decision-making processes in real-time energy markets. The study integrates critical operational constraints, such as battery lifespan limitations and dynamic energy pricing, enabling sustainable and adaptive energy management while maintaining high-quality optimization outcomes. Extensive simulations demonstrate the effectiveness of the proposed framework in balancing energy acquisition, storage, and dispatch decisions across diverse DERs, contributing to smarter energy distribution systems. What is the implication of the main findings? The enhanced computational efficiency and scalability of the parallelized SA approach make it a practical and advanced solution for real-time VPP scheduling, ensuring reliable and adaptive energy management in dynamic market environments. This framework supports the development of future smart city energy systems-as evidenced by its strategic deployment in Portugal in the national project New Generation Storage (NGS)-by enabling the seamless integration of renewable energy sources, electric vehicles, and energy storage systems, ultimately contributing to a resilient, low-carbon energy ecosystem. By optimizing energy distribution with a focus on maximizing social welfare, the framework aligns with the principles of economic sustainability, grid stability, and environmental responsibility in smart cities. The findings highlight the transformative role of HPC and parallel computing in addressing the growing complexity of energy systems, providing a scalable and efficient blueprint for enhancing energy optimization and distribution in urban settings.Highlights The integration of advanced computational techniques into energy optimization is crucial to building sustainable and efficient urban energy systems. VPPs play a key role in smart cities by enabling the seamless management of renewable energy resources, energy storage, and electric vehicles. This study demonstrates how HPC and parallelized SA can optimize energy distribution, improve grid stability, and maximize social welfare. By addressing challenges like battery lifespan and real-time energy trading, this work supports the development of smarter, more sustainable cities with resilient and equitable energy systems. What are the main findings? A novel parallelized SA algorithm was developed and implemented by using OpenMP on HPC infrastructure, delivering a substantial reduction in computational time for VPP scheduling. The approach showcases significant scalability, efficiently optimizing large-scale VPP networks with up to 512 prosumers, ensuring that the system remains robust as network complexity and size increase. By achieving near-linear speedup ratios across up to 32 cores, the algorithm leverages parallel computing to accelerate decision-making processes in real-time energy markets. The study integrates critical operational constraints, such as battery lifespan limitations and dynamic energy pricing, enabling sustainable and adaptive energy management while maintaining high-quality optimization outcomes. Extensive simulations demonstrate the effectiveness of the proposed framework in balancing energy acquisition, storage, and dispatch decisions across diverse DERs, contributing to smarter energy distribution systems. What is the implication of the main findings? The enhanced computational efficiency and scalability of the parallelized SA approach make it a practical and advanced solution for real-time VPP scheduling, ensuring reliable and adaptive energy management in dynamic market environments. This framework supports the development of future smart city energy systems-as evidenced by its strategic deployment in Portugal in the national project New Generation Storage (NGS)-by enabling the seamless integration of renewable energy sources, electric vehicles, and energy storage systems, ultimately contributing to a resilient, low-carbon energy ecosystem. By optimizing energy distribution with a focus on maximizing social welfare, the framework aligns with the principles of economic sustainability, grid stability, and environmental responsibility in smart cities. The findings highlight the transformative role of HPC and parallel computing in addressing the growing complexity of energy systems, providing a scalable and efficient blueprint for enhancing energy optimization and distribution in urban settings.Abstract 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.
