An adaptive current–voltage step size solver for accurate and efficient modeling of solar photovoltaic modules

Accurate modeling of photovoltaic (PV) modules is critical for system design, energy yield prediction, and maximum power point tracking (MPPT). Conventional single-diode models (SDM) provide a balance between simplicity and physical realism but require iterative numerical methods to solve the implicit current–voltage (I–V) equation, often leading to slow convergence and reduced stability under dynamic conditions. This paper introduces a novel adaptive current–voltage step-size solver (AIVSS) that dynamically adjusts both voltage and current increments based on the local slope of the I–V curve, thereby improving numerical accuracy and convergence efficiency. The proposed method was validated through 500 simulations under standard test conditions (STC), partial shading, high temperature, and dynamic irradiance, and was compared with the conventional adaptive voltage-step solver (AVSS). Performance was evaluated using mean absolute error (MAE), root mean square error (RMSE), iteration count, and computation time. Results show that AIVSS reduced the average iteration count by 35–40%, decreased the average MAE from 6.15 W to 2.18 W, and reduced the average RMSE from 7.68 W to 2.85 W across all operating conditions. In addition, the proposed method achieved faster convergence and smoother dynamic response under rapidly changing irradiance. These findings confirm the strong potential of AIVSS for real-time PV system simulation and MPPT applications, providing a robust and computationally efficient tool for accurate PV module characterization.

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Publication Details

Journal
PLoS ONE
Published
2026-10-06
DOI
https://doi.org/10.1371/journal.pone.0359796
Primary Topic
Photovoltaic System Optimization Techniques
Type
article
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article

An adaptive current–voltage step size solver for accurate and efficient modeling of solar photovoltaic modules

Baraka Kichonge, Nsulwa John Mlazi
PLoS ONE
Photovoltaic System Optimization Techniques
article

An adaptive current–voltage step size solver for accurate and efficient modeling of solar photovoltaic modules

Baraka Kichonge, Nsulwa John Mlazi
article en

Abstract

Accurate modeling of photovoltaic (PV) modules is critical for system design, energy yield prediction, and maximum power point tracking (MPPT). Conventional single-diode models (SDM) provide a balance between simplicity and physical realism but require iterative numerical methods to solve the implicit current–voltage (I–V) equation, often leading to slow convergence and reduced stability under dynamic conditions. This paper introduces a novel adaptive current–voltage step-size solver (AIVSS) that dynamically adjusts both voltage and current increments based on the local slope of the I–V curve, thereby improving numerical accuracy and convergence efficiency. The proposed method was validated through 500 simulations under standard test conditions (STC), partial shading, high temperature, and dynamic irradiance, and was compared with the conventional adaptive voltage-step solver (AVSS). Performance was evaluated using mean absolute error (MAE), root mean square error (RMSE), iteration count, and computation time. Results show that AIVSS reduced the average iteration count by 35–40%, decreased the average MAE from 6.15 W to 2.18 W, and reduced the average RMSE from 7.68 W to 2.85 W across all operating conditions. In addition, the proposed method achieved faster convergence and smoother dynamic response under rapidly changing irradiance. These findings confirm the strong potential of AIVSS for real-time PV system simulation and MPPT applications, providing a robust and computationally efficient tool for accurate PV module characterization.

PLoS ONEVol. 21(10)
Nelson Mandela African Institution of Science and Technology (TZ)
Openalex Percentile: Top 33%
Photovoltaic System Optimization Techniques
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An adaptive current–voltage step size solver for accurate and efficient modeling of solar photovoltaic modules — Baraka Kichonge, Nsulwa John Mlazi · PLoS ONE (2026) | TGRS Research Map | TGRS