Paper ID: 1535

Stochastic–Dynamic Modelling of Performance Degradation in a Grid-Connected Rooftop Photovoltaic System

 

Dani Yuniawan1*, Vetty Kartikasari1, Rahman Arifuddin2, Effendi Mohamad3

1Department of Industrial Engineering, Faculty of Engineering, Universitas Merdeka Malang, Malang 65146, Indonesia

2Department of Electrical Engineering, Faculty of Engineering, Universitas Merdeka Malang, Malang 65146, Indonesia

3Faculty of Industrial and Manufacturing Technology and Engineering, Universiti Teknikal Malaysia Melaka, Malaysia

 

*Corresponding author: dani.yuniawan@unmer.ac.id

 

Abstract

The present study examines how climatic variability and long-term degradation influence the technical performance of a 100-kWp rooftop photovoltaic (rPV) system that has been installed at a university campus in a humid tropical region. The analysis was conducted using an observational dataset spanning six calendar years (2020–2025), specifically 65 months. The present study integrated Monte Carlo (MC) simulation (10,000 iterations) with a dynamic feedback structure to represent the system’s degradation and reliability over a 25-year operational horizon. The stochastic simulation revealed that annual energy yield is not the only deterministic indicator of degradation and reliability, with values within 111–129 MWh yr⁻¹ (P10–P90) and a median specific energy yield close to 1200 kWh kWp⁻¹ yr⁻¹. The results revealed that the performance ratio (PR) stabilised between 0.75–0.80, which is consistent with empirical rPV systems in humid tropical conditions. In a literature-informed two-stage dynamic degradation scenario (2S-DDS) of 0.55–1.00% yr⁻¹, the model estimated a cumulative lifetime energy loss of approximately 8.7%, relative to a no-degradation baseline. Meanwhile, the results of the sensitivity analysis revealed that the PR residual has the largest total-order contribution to annual energy yield variance, followed by irradiance, temperature, and degradation. It is noteworthy that the proposed framework is methodologically novel as it (1) used correlation-preserving paired bootstrap resampling to maintain the empirical irradiance-temperature covariance, (2) directly embedded a 2S-DDS in the probabilistic MC structure, and (3) created a modelling architecture that can be applied to PV systems in humid tropical regions. Apart from that, the proposed framework extends deterministic estimation by quantifying conditional energy yield uncertainties and time-dependent system.

Keywords: energy yield uncertainty; Monte Carlo simulation; performance ratio; stochastic-dynamic modelling; degradation modelling; campus rooftop PV; tropical climate.

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