Optimizing Virtual Power Plants with Parallel Simulated Annealing on High-Performance Computing
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)...
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MDPI AG
2025-03-01
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| Series: | Smart Cities |
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| Online Access: | https://www.mdpi.com/2624-6511/8/2/47 |
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| author | Ali Abbasi Filipe Alves Rui A. Ribeiro João L. Sobral Ricardo Rodrigues |
| author_facet | Ali Abbasi Filipe Alves Rui A. Ribeiro João L. Sobral Ricardo Rodrigues |
| author_sort | Ali Abbasi |
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| description | 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. |
| format | Article |
| id | doaj-art-dd1f57c6dbcc4d499dc8f013bbfaa17c |
| institution | DOAJ |
| issn | 2624-6511 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Smart Cities |
| spelling | doaj-art-dd1f57c6dbcc4d499dc8f013bbfaa17c2025-08-20T03:13:54ZengMDPI AGSmart Cities2624-65112025-03-01824710.3390/smartcities8020047Optimizing Virtual Power Plants with Parallel Simulated Annealing on High-Performance ComputingAli Abbasi0Filipe Alves1Rui A. Ribeiro2João L. Sobral3Ricardo Rodrigues4DTx—Digital Transformation CoLAB, University of Minho, 4800-058 Guimarães, PortugalDTx—Digital Transformation CoLAB, University of Minho, 4800-058 Guimarães, PortugalDTx—Digital Transformation CoLAB, University of Minho, 4800-058 Guimarães, PortugalCentro de Algoritmi, Universidade do Minho, Campus of Gualar, 4704-553 Braga, PortugalDTx—Digital Transformation CoLAB, University of Minho, 4800-058 Guimarães, PortugalThis 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.https://www.mdpi.com/2624-6511/8/2/47virtual power plantsocial welfare optimizationbattery lifespan constraintsparallel simulated annealinghigh-performance computingmixed-integer linear programming |
| spellingShingle | Ali Abbasi Filipe Alves Rui A. Ribeiro João L. Sobral Ricardo Rodrigues Optimizing Virtual Power Plants with Parallel Simulated Annealing on High-Performance Computing Smart Cities virtual power plant social welfare optimization battery lifespan constraints parallel simulated annealing high-performance computing mixed-integer linear programming |
| title | Optimizing Virtual Power Plants with Parallel Simulated Annealing on High-Performance Computing |
| title_full | Optimizing Virtual Power Plants with Parallel Simulated Annealing on High-Performance Computing |
| title_fullStr | Optimizing Virtual Power Plants with Parallel Simulated Annealing on High-Performance Computing |
| title_full_unstemmed | Optimizing Virtual Power Plants with Parallel Simulated Annealing on High-Performance Computing |
| title_short | Optimizing Virtual Power Plants with Parallel Simulated Annealing on High-Performance Computing |
| title_sort | optimizing virtual power plants with parallel simulated annealing on high performance computing |
| topic | virtual power plant social welfare optimization battery lifespan constraints parallel simulated annealing high-performance computing mixed-integer linear programming |
| url | https://www.mdpi.com/2624-6511/8/2/47 |
| work_keys_str_mv | AT aliabbasi optimizingvirtualpowerplantswithparallelsimulatedannealingonhighperformancecomputing AT filipealves optimizingvirtualpowerplantswithparallelsimulatedannealingonhighperformancecomputing AT ruiaribeiro optimizingvirtualpowerplantswithparallelsimulatedannealingonhighperformancecomputing AT joaolsobral optimizingvirtualpowerplantswithparallelsimulatedannealingonhighperformancecomputing AT ricardorodrigues optimizingvirtualpowerplantswithparallelsimulatedannealingonhighperformancecomputing |