To optimize the energy output of wave energy converters(WECs)in complex ocean environments,a novel multi-objective robust-stochastic strategy that integrates uncertainty modeling to address the dynamics of ocean waves...To optimize the energy output of wave energy converters(WECs)in complex ocean environments,a novel multi-objective robust-stochastic strategy that integrates uncertainty modeling to address the dynamics of ocean waves is presented.We introduce the dynamic order adaptive Runge-Kutta(DOARK)method for more efficient solution of kinetic equations.The optimization strategy seeks to maximize power output while minimizing systematic damage.First,we develop kinetic formulations for the proposed WEC and incorporate stochastic terms for a more accurate description in volatile conditions.The control process is optimized using a multiobjective approach with a cost function that balances output power and damage,solved via the-constraint method.An adaptive algorithm is applied to adjust step size,enhancing the Runge-Kutta method.In our approach,step size is iterated based on damping coefficient ranges.Simulation results demonstrate that the proposed strategy improves output power by 12.34%and reduces systematic damage by 15.65%,compared with traditional methods,which demonstrates the advantage of the proposed method.展开更多
The application of floating photovoltaics (PVs) in hydropower plants has gained increasing interest in forming hybrid energy systems (HESs). It enhances the operational benefits of the existing hydropower plants. Howe...The application of floating photovoltaics (PVs) in hydropower plants has gained increasing interest in forming hybrid energy systems (HESs). It enhances the operational benefits of the existing hydropower plants. However, uncertainties of PV and load powers can present great challenges to scheduling HESs. To address these uncertainties, this paper proposes a novel two-stage optimization approach that combines distributionally robust chance-constrained (DRCC) and robust-stochastic optimization (RSO) approaches to minimize the operational cost of an HES. In the first stage, the scheduling of each device is obtained via the DRCC approach considering the PV power and load forecast errors. The second stage provides a robust near real time energy dispatch according to different scenarios of PV power and load demand. The solution of the RSO problem is obtained via a novel double-layer particle swarm optimization algorithm. The performance of the proposed approach is compared to the traditional stochastic and robust-stochastic approaches. Simulation results de- monstrate the superiority of the proposed two-stage approach and its solution method in terms of operational cost and execution time.展开更多
文摘To optimize the energy output of wave energy converters(WECs)in complex ocean environments,a novel multi-objective robust-stochastic strategy that integrates uncertainty modeling to address the dynamics of ocean waves is presented.We introduce the dynamic order adaptive Runge-Kutta(DOARK)method for more efficient solution of kinetic equations.The optimization strategy seeks to maximize power output while minimizing systematic damage.First,we develop kinetic formulations for the proposed WEC and incorporate stochastic terms for a more accurate description in volatile conditions.The control process is optimized using a multiobjective approach with a cost function that balances output power and damage,solved via the-constraint method.An adaptive algorithm is applied to adjust step size,enhancing the Runge-Kutta method.In our approach,step size is iterated based on damping coefficient ranges.Simulation results demonstrate that the proposed strategy improves output power by 12.34%and reduces systematic damage by 15.65%,compared with traditional methods,which demonstrates the advantage of the proposed method.
文摘The application of floating photovoltaics (PVs) in hydropower plants has gained increasing interest in forming hybrid energy systems (HESs). It enhances the operational benefits of the existing hydropower plants. However, uncertainties of PV and load powers can present great challenges to scheduling HESs. To address these uncertainties, this paper proposes a novel two-stage optimization approach that combines distributionally robust chance-constrained (DRCC) and robust-stochastic optimization (RSO) approaches to minimize the operational cost of an HES. In the first stage, the scheduling of each device is obtained via the DRCC approach considering the PV power and load forecast errors. The second stage provides a robust near real time energy dispatch according to different scenarios of PV power and load demand. The solution of the RSO problem is obtained via a novel double-layer particle swarm optimization algorithm. The performance of the proposed approach is compared to the traditional stochastic and robust-stochastic approaches. Simulation results de- monstrate the superiority of the proposed two-stage approach and its solution method in terms of operational cost and execution time.
