Energy Storage Systems in Micro-Grid of Hybrid Renewable Energy Solutions

This research evaluates Battery Energy Storage Systems (BESS) and Compressed Air Vessels (CAV) as complementary solutions for enhancing micro-grid resilience, flexibility, and sustainability. BESS units ranging from 5 to 400 kWh were modeled using a Nonlinear Autoregressive Neural Network with Exogenous Inputs (NARX) neural network, achieving high SOC prediction accuracy with R2 > 0.98 and MSE as low as 0.13 kWh2. Larger batteries (400–800 kWh) effectively reduced grid purchases and redistributed surplus energy, improving system efficiency. CAVs were tested in pumped-storage mode, achieving 33.9–57.1% efficiency under 0.5–2 bar and high head conditions, offering long-duration, low-degradation storage. Waterhammer-induced CAV storage demonstrated reliable pressure capture when Reynolds number ≤ 75,000 and Volume Fraction Ratio, VFR > 11%, with a prototype reaching 6142 kW and 170 kWh at 50% air volume. CAVs proved modular, scalable, and environmentally robust, suitable for both energy and water management. Hybrid systems combining BESS and CAVs offer strategic advantages in balancing renewable intermittency. Machine learning and hydraulic modeling support intelligent control and adaptive dispatch. Together, these technologies enable future-ready micro-grids aligned with sustainability and grid stability goals.

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Publication Details

Journal
Technologies
Published
2025-11-14
DOI
https://doi.org/10.3390/technologies13110527
Citations
1
Primary Topic
Microgrid Control and Optimization
Type
article
Field-Weighted Citation Impact
1.21
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article

Energy Storage Systems in Micro-Grid of Hybrid Renewable Energy Solutions

Armando Carravetta, Helena Margarida Ramos, Óscar E. Coronado-Hernández, Oreste Fecarotta et al.
1 citations
Technologies
Microgrid Control and Optimization
1.21
article

Energy Storage Systems in Micro-Grid of Hybrid Renewable Energy Solutions

Armando Carravetta, Helena Margarida Ramos, Óscar E. Coronado-Hernández, Oreste Fecarotta, Modesto Pérez‐Sánchez, Mohsen Besharat
article en
1 citations

Abstract

This research evaluates Battery Energy Storage Systems (BESS) and Compressed Air Vessels (CAV) as complementary solutions for enhancing micro-grid resilience, flexibility, and sustainability. BESS units ranging from 5 to 400 kWh were modeled using a Nonlinear Autoregressive Neural Network with Exogenous Inputs (NARX) neural network, achieving high SOC prediction accuracy with R2 > 0.98 and MSE as low as 0.13 kWh2. Larger batteries (400–800 kWh) effectively reduced grid purchases and redistributed surplus energy, improving system efficiency. CAVs were tested in pumped-storage mode, achieving 33.9–57.1% efficiency under 0.5–2 bar and high head conditions, offering long-duration, low-degradation storage. Waterhammer-induced CAV storage demonstrated reliable pressure capture when Reynolds number ≤ 75,000 and Volume Fraction Ratio, VFR > 11%, with a prototype reaching 6142 kW and 170 kWh at 50% air volume. CAVs proved modular, scalable, and environmentally robust, suitable for both energy and water management. Hybrid systems combining BESS and CAVs offer strategic advantages in balancing renewable intermittency. Machine learning and hydraulic modeling support intelligent control and adaptive dispatch. Together, these technologies enable future-ready micro-grids aligned with sustainability and grid stability goals.

TechnologiesVol. 13(11)
University of Leeds (GB), University of Cartagena (CO), Instituto Superior de Tecnologias Avançadas (PT), Universitat Politècnica de València (ES)
Openalex Percentile: Top 15%
Microgrid Control and Optimization
1.21
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Energy Storage Systems in Micro-Grid of Hybrid Renewable Energy Solutions — Armando Carravetta, Helena Margarida Ramos, et al. · Technologies (2025) | TGRS Research Map | TGRS