The main current approaches for generation of the packed bed models are based on rigid body dynamics(RBD)and Newton's laws(discrete element methods-DEM).This paper deals with the development and analysis of a nove...The main current approaches for generation of the packed bed models are based on rigid body dynamics(RBD)and Newton's laws(discrete element methods-DEM).This paper deals with the development and analysis of a novel code based on analytical geometry approach for the packed bed generation.The architecture and main algorithms of the novel code are described and clarified.The parameters of the packed bed generated via the novel code are compared with experimental data and packed beds generated via Blender(RBD),Yade(DEM).The novel code demonstrates many advantages,such as good correlation with experimental data,no overlaps between pellets in the packed bed,and a low computational time for packed bed generation.The packed bed model can be directly exported in.step format.Other advantages are also demonstrated and clarified.The novel code is attached to this paper and can be freely used by engineers and scientists.展开更多
Hybrid microgrids that integrate solar and wind energy with diesel generators are widely recognized as efficient alternatives for reducing fuel reliance and achieving energy resilience in remote or off-grid areas.None...Hybrid microgrids that integrate solar and wind energy with diesel generators are widely recognized as efficient alternatives for reducing fuel reliance and achieving energy resilience in remote or off-grid areas.Nonetheless,the optimal design of these systems presents technical and economic hurdles stemming from variable renewable resources,spatial constraints,and escalating fuel costs.This study presents a 30-year economic optimization of hybrid diesel-wind-solar microgrids,ensuring operational reliability and compliance with land use restrictions.A Python-based model was created using two restricted nonlinear optimization methods:sequential least squares programming(SLSQP)and constrained optimization by linear approximation(COBYLA).The model reduces overall system expenses,comprising capital investment,operational and maintenance costs,and fuel expenditures,by modulating diesel power production and calibrating renewable capacity within defined parameters.The findings indicate that optimal designs can decrease system expenses by more than $1.5 billion relative to high diesel baseline systems.The SLSQP technique attained a renewable energy proportion of 33%,illustrating the efficacy of direct optimization in developing economical,space-limited hybrid energy systems.展开更多
文摘The main current approaches for generation of the packed bed models are based on rigid body dynamics(RBD)and Newton's laws(discrete element methods-DEM).This paper deals with the development and analysis of a novel code based on analytical geometry approach for the packed bed generation.The architecture and main algorithms of the novel code are described and clarified.The parameters of the packed bed generated via the novel code are compared with experimental data and packed beds generated via Blender(RBD),Yade(DEM).The novel code demonstrates many advantages,such as good correlation with experimental data,no overlaps between pellets in the packed bed,and a low computational time for packed bed generation.The packed bed model can be directly exported in.step format.Other advantages are also demonstrated and clarified.The novel code is attached to this paper and can be freely used by engineers and scientists.
文摘Hybrid microgrids that integrate solar and wind energy with diesel generators are widely recognized as efficient alternatives for reducing fuel reliance and achieving energy resilience in remote or off-grid areas.Nonetheless,the optimal design of these systems presents technical and economic hurdles stemming from variable renewable resources,spatial constraints,and escalating fuel costs.This study presents a 30-year economic optimization of hybrid diesel-wind-solar microgrids,ensuring operational reliability and compliance with land use restrictions.A Python-based model was created using two restricted nonlinear optimization methods:sequential least squares programming(SLSQP)and constrained optimization by linear approximation(COBYLA).The model reduces overall system expenses,comprising capital investment,operational and maintenance costs,and fuel expenditures,by modulating diesel power production and calibrating renewable capacity within defined parameters.The findings indicate that optimal designs can decrease system expenses by more than $1.5 billion relative to high diesel baseline systems.The SLSQP technique attained a renewable energy proportion of 33%,illustrating the efficacy of direct optimization in developing economical,space-limited hybrid energy systems.
