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Hybrid renewable energy microgrid optimization: an analysis of system performance and cost-efficiency using Python-generated custom code for diesel-wind-solar configurations.

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Bibliographic Details
Title: Hybrid renewable energy microgrid optimization: an analysis of system performance and cost-efficiency using Python-generated custom code for diesel-wind-solar configurations.
Authors: Abbass, Amr
Source: Energy Storage & Saving; Dec2025, Vol. 4 Issue 4, p392-403, 12p
Subject Terms: HYBRID power systems, OPTIMIZATION algorithms, PYTHON programming language, MICROGRIDS, LINEAR programming, HYBRID solar energy systems, ECONOMIC efficiency, ENERGY industries
Abstract: 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. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract: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. [ABSTRACT FROM AUTHOR]
ISSN:20973047
DOI:10.1016/j.enss.2025.07.001