A Comprehensive Method for Optimum Sizing of Hybrid Energy Systems using Intelligence Evolutionary Algorithms

Rouhani, Ahmad

doi:10.17485/ijst/2013/v6i6.3

Cited by 20 publications

(4 citation statements)

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“…Для решения задач оптимизации состава оборудования HRES преимущественно используются упрощенные «энергетические» модели компонентов, однозначно связывающие их выходные энергетические характеристики с внешними факторами и входными воздействиями [12,14,17].…”

Section: модели компонентовunclassified

Optimization of Equipment Composition of Hybrid Energy Systems with Renewable Energy Sources

Обухов¹,

Ибрагим²

2020

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Section: модели компонентовunclassified

Optimization of Equipment Composition of Hybrid Energy Systems with Renewable Energy Sources

Обухов¹,

Ибрагим²

2020

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“…These hybrid systems require optimal component selections and settings, as the choices must consider both the climatic and economic conditions encountered in different regions. For instance, Rouhani et al [10] utilized the genetic algorithm and particle swarm optimization to optimize a hybrid power system consisting of PVs, WTs, batteries, and fuel cells to achieve system reliability while minimizing system costs. N'guessan et al [11] employed the non-dominated sorting genetic algorithm to find the optimal configuration for an off-grid power system composed of WTs, a PEMFC, electrolyzers, batteries, and supercapacitors.…”

Section: Introductionmentioning

confidence: 99%

Extended-Window Algorithms for Model Prediction Applied to Hybrid Power Systems

Wang,

Huang

2024

Technologies

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This paper proposes extended-window algorithms for model prediction and applies them to optimize hybrid power systems. We consider a hybrid power system comprising solar panels, batteries, a fuel cell, and a chemical hydrogen generation system. The proposed algorithms enable the periodic updating of prediction models and corresponding changes in system parts and power management based on the accumulated data. We first develop a hybrid power model to evaluate system responses under different conditions. We then build prediction models using five artificial intelligence algorithms. Among them, the light gradient boosting machine and extreme gradient boosting methods achieve the highest accuracies for predicting solar radiation and load responses, respectively. Therefore, we apply these two models to forecast solar and load responses. Third, we introduce extended-window algorithms and investigate the effects of window sizes and replacement costs on system performance. The results show that the optimal window size is one week, and the system cost is 13.57% lower than the cost of the system that does not use the extended-window algorithms. The proposed method also tends to make fewer component replacements when the replacement cost increases. Finally, we design experiments to demonstrate the feasibility and effectiveness of systems using extended-window model prediction.

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“…Prototyping and testing of each design combination is cumbersome, expensive, and time-consuming. Therefore, modelling and simulation are indispensable for the concept evaluation, prototyping and analysis of HEVs [30]. The main advantage of hybrid technology is the possibility of increasing the operational efficiency of the powertrain and regenerating the braking energy using an on-board energy storage system [31].…”

Section: Introductionmentioning

confidence: 99%

Impact of the hybrid electric architecture on the performance and emissions of a delivery truck with a dual-fuel RCCI engine

et al. 2021

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A Comprehensive Method for Optimum Sizing of Hybrid Energy Systems using Intelligence Evolutionary Algorithms

Cited by 20 publications

References 0 publications

Optimization of Equipment Composition of Hybrid Energy Systems with Renewable Energy Sources

Optimization of Equipment Composition of Hybrid Energy Systems with Renewable Energy Sources

Extended-Window Algorithms for Model Prediction Applied to Hybrid Power Systems

Impact of the hybrid electric architecture on the performance and emissions of a delivery truck with a dual-fuel RCCI engine

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