Reliability-Based System Modelling for Renewable Energy Mini-grids Development

Nwosu, D (2026) Reliability-Based System Modelling for Renewable Energy Mini-grids Development. Doctoral thesis, Liverpool John Moores University.

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Abstract

Solar Energy Mini-Grids (SEMs) are increasingly deployed as decentralised electrification solutions in regions where conventional grid expansion remains technically, economically, or institutionally constrained. Although existing SEM design and optimisation frameworks have advanced system planning significantly, most remain fundamentally deterministic, assuming continuous subsystem availability and thereby neglecting the stochastic failure behaviour of critical components. This assumption leads to systematically optimistic estimates of system performance, particularly with respect to the Total Electrical Load Served (TELS), which is a key indicator of service reliability, operational effectiveness, and financial viability.

This thesis addresses the absence of a practical reliability-integrated modelling framework capable of linking subsystem failure behaviour directly to system-level energy delivery. The research develops a SEM Reliability-Based Regression Model (SEM-RBRM) to predict actual, time-varying TELS over the operational lifetime of an SEM by explicitly incorporating subsystem reliability into performance modelling. The proposed framework combines deterministic SEM simulation, multivariate regression modelling, and reliability engineering analysis within a unified analytical structure.

A baseline deterministic SEM model was first developed using Hybrid Optimization of Multiple Energy Resources (HOMER) to simulate the performance of a hybrid photovoltaic–battery–diesel–converter configuration under varying environmental and load conditions. Time-series simulation outputs were then used to formulate a regression-based performance model relating TELS to key operational variables. The resulting model demonstrated predictive accuracy of R2≈0.99, confirming that inverter output, generator contribution, and battery power explain most of the variability in deterministic SEM performance. However, this model represents an upper-bound performance estimate because it does not account for subsystem failure, degradation, or downtime.

To address this limitation, subsystem reliability was modelled using exponential reliability functions and integrated via a Reliability Block Diagram (RBD) representation of SEM architecture. This enabled the formulation of a reliability-adjusted performance function in which effective load served is expressed as the product of deterministic TELS and system availability over time. The results show that subsystem reliability exerts a measurable and progressive constraining effect on energy delivery, and that deterministic models consistently overestimate long-term SEM performance. Sensitivity analysis further identified the power converter as the dominant critical-path component due to its series position within the system topology.

By integrating reliability engineering directly into SEM performance prediction, this thesis contributes a computationally efficient, scalable, and decision-relevant framework for reliability-aware mini-grid design, evaluation, and long-term planning.

Item Type:	Thesis (Doctoral)
Uncontrolled Keywords:	Reliability; Regression; Modelling; Simulation; Sensitivity Analysis; Normalisation; Interpolation; Monte Carlo Simulation; Failure Rate; Reliability Block Diagram; Physics of Failure; Markov Reliability Model; REopt; RETScreen; Mixed-Integer Linear Programming; ybrid Optimization of Multiple Energy Resources (HOMER); Solar Energy Mini-grids (SEMs); Total Electric Load Served (TELS); Photovoltaic Module; Converter; Battery; Balance of System; Awka; Liverpool
Subjects:	Q Science > QA Mathematics > QA76 Computer software T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Engineering and Built Environment
Date of acceptance:	17 September 2026
Date of first compliant Open Access:	5 October 2026
Date Deposited:	05 Oct 2026 08:57
Last Modified:	05 Oct 2026 08:58
DOI or ID number:	10.24377/LJMU.t.00029591
Supervisors:	Kotadia, H, Wang, J, Ekere, N and Bashir, M
URI:	https://researchonline.ljmu.ac.uk/id/eprint/29591

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