Performance Evaluation and Stability Analysis of A PV-Plus-Storage Micro-Grid System Using Descriptive Statistics, Correlation, and Relative Weight Methods

Baraka Emmanuel; Mbazingwa E. Mkiramweni

doi:10.37284/eaje.8.2.3921

Baraka Emmanuel Dar es Salaam Institute of Technology
Mbazingwa E. Mkiramweni Dar es Salaam Institute of Technology

DOI: https://doi.org/10.37284/eaje.8.2.3921

Keywords: PV-Plus-Storage Micro-Grid, Solar Irradiance, Battery State of Charge (SOC), Battery Efficiency, Load Demand, Operation and Maintenance (O&M) Cost, Downtime, Descriptive Statistics, Correlation Analysis, Relative Weight Method, Stability Analysis, Predictive Maintenance

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Abstract

This study evaluates the operational performance and stability of a PV-plus-storage micro-grid system at Yas Cell Towers over an eight-month period. Using descriptive statistics, correlation analysis, and relative weight methods, key system parameters, including solar irradiance, PV output, load demand, battery state of charge (SOC), battery efficiency, downtime, and operation and maintenance (O&M) costs, were analysed to identify performance trends and critical reliability factors. Results indicate an average solar irradiance of 5.39 kWh/m²/day with 80.8% steadiness, and PV generation closely matches the average load demand of 36.49 kWh/day. Battery efficiency remained high (mean = 90.6%, SD = 3.42%), though SOC exhibited significant fluctuations (40.3–80.5%), highlighting opportunities for improved storage management. Correlation analysis shows a strong positive association between PV output and load demand (r = 0.960, p < 0.01) and a negative correlation with battery SOC (r = -0.769, p < 0.05). Relative weight analysis identified maintenance activities, PV generation, and battery SOC as primary factors influencing system reliability and cost efficiency. Stability index ranking revealed O&M costs and downtime as the most consistent variables, while battery efficiency demonstrated the highest variability, indicating areas for focused monitoring. Overall, the system demonstrates reliable performance and effective energy conversion, but enhanced predictive maintenance and SOC management could further optimise operational reliability and economic efficiency

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