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.
