Experimental digital twin for MPPT-based load regulation and energy analytics in an isolated DC microgrid
DOI:
https://doi.org/10.24054/rcta.v1i47.4291Keywords:
digital twin, MPPT, data science, DC microgrid, photovoltaic energy, ESP32Abstract
The development of data-driven control and monitoring strategies is a key factor in optimizing the energy performance of photovoltaic direct-current (DC) microgrids. This paper presents the implementation of an experimental digital twin for load regulation using Maximum Power Point Tracking (MPPT) technology, complemented by an energy analytics module to evaluate system efficiency and stability. The proposed methodology integrates a Matlab-Simulink simulation with a physical implementation on an ESP32 microcontroller, executing a Perturb and Observe (P&O) algorithm coupled to a Boost DC/DC converter. Electrical and environmental data are processed using data science tools (Python, Pandas, NumPy, Seaborn) to determine correlations among irradiance, temperature, voltage, and power. Experimental results demonstrate an overall efficiency of 91.6 % under real irradiance conditions and a 99.7 % correlation between simulation and physical measurement, validating the reliability of the digital twin. The hybrid approach combines electronic control, digital modeling, and statistical analysis, establishing a low-cost intelligent energy monitoring platform suitable for rural microgrids and educational environments.
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References
A. Ali, et al., “Investigation of MPPT Techniques Under Uniform and Non-Uniform Solar Irradiation Condition–A Retrospection,” IEEE Access, vol. 8, pp. 127368–127392, 2020, doi: 10.1109/ACCESS.2020.3007710.
L. Shang, H. Guo, and W. Zhu, “An Improved MPPT Control Strategy Based on Incremental Conductance Algorithm,” Protection and Control of Modern Power Systems, vol. 5, no. 2, pp. 1–8, Apr. 2020, doi: 10.1186/s41601-020-00161-z.
L. Liu, C. Huang, J. Mu, J. Cheng, and Z. Zhu, “A P&O MPPT With a Novel Analog Power-Detector for WSNs Applications,” IEEE Trans. Circuits Syst. II: Express Briefs, vol. 67, no. 10, pp. 1680–1684, Oct. 2020, doi: 10.1109/TCSII.2019.2940212.
C. Bae, E. Choi, and S. Lee, “Technologies, Applications, and Challenges of Digital Twin Across Industries: A Systematic Review of the State-of-the-Art Literature,” IEEE Access, vol. 13, pp. 152843–152869, 2025, doi: 10.1109/ACCESS.2025.3601615.
Y. Lee, M.-S. Baek, and K. Yoon, “Digital Entity Management Methodology for Digital Twin Implementation: Concept, Definition, and Examples,” IEEE Trans. Broadcasting, vol. 71, no. 1, pp. 19–29, Mar. 2025, doi: 10.1109/TBC.2024.3517138.
MathWorks, “The MathWorks / MATLAB – Simulink: MPPT Algorithm,” 2022. [Online]. Available: https://la.mathworks.com/solutions/electrification/mppt-algorithm.html
W. Wang, M. Liu, and J. Li, “Research and Realization of Virtual-Real Control of Robot System for Off-Heap Detector Assisted Installation Based on Digital Twin,” IEEE Journal of Radio Frequency Identification, vol. 6, pp. 810–814, 2022, doi: 10.1109/JRFID.2022.3209715.
Y. Zhou, et al., “Digital Twins Visualization of Large Electromechanical Equipment,” IEEE Journal of Radio Frequency Identification, vol. 6, pp. 993–997, 2022, doi: 10.1109/JRFID.2022.3217123.
A. Boumaiza, A. Sanfilippo, and N. Mohandes, “Modeling multi-criteria decision analysis in residential PV adoption,” Energy Strategy Reviews, vol. 39, art. 100789, Jan. 2022, doi: 10.1016/j.esr.2021.100789.
B. Zhang, M. Zhang, T. Dong, M. Lu, and H. Li, “Design of Digital Twin System for DC Contactor Condition Monitoring,” IEEE Trans. Industry Applications, vol. 59, no. 4, pp. 3904–3909, Jul.–Aug. 2023, doi: 10.1109/TIA.2023.3256978.
M. R. Javed, A. Waleed, U. S. Virk, and S. Z. ul Hassan, “Comparison of the Adaptive Neural-Fuzzy Interface System (ANFIS) Based Solar Maximum Power Point Tracking (MPPT) with Other Solar MPPT Methods,” in Proc. IEEE 23rd Int. Multitopic Conf. (INMIC), Bahawalpur, Pakistan, 2020, pp. 1–5, doi: 10.1109/INMIC50486.2020.9318178.
S. Xu, R. Shao, B. Cao, and L. Chang, “Single-Phase Grid-Connected PV System With Golden Section Search-Based MPPT Algorithm,” Chinese Journal of Electrical Engineering, vol. 7, no. 4, pp. 25–36, Dec. 2021, doi: 10.23919/CJEE.2021.000035.
O. Abdel-Rahim and H. Wang, “A New High Gain DC-DC Converter With Model-Predictive-Control Based MPPT Technique for Photovoltaic Systems,” CPSS Trans. Power Electron. Appl., vol. 5, no. 2, pp. 191–200, Jun. 2020, doi: 10.24295/CPSSTPEA.2020.00016.
S. Uprety and H. Lee, “A 0.65-mW-to-1-W Photovoltaic Energy Harvester With Irradiance-Aware Auto-Configurable Hybrid MPPT Achieving >95% MPPT Efficiency and 2.9-ms FOCV Transient Time,” IEEE J. Solid-State Circuits, vol. 56, no. 6, pp. 1827–1836, Jun. 2021, doi: 10.1109/JSSC.2020.3042753.
J. Maeng, J. Jeong, I. Park, M. Shim, and C. Kim, “A Time-Based Direct MPPT Technique for Low-Power Photovoltaic Energy Harvesting,” IEEE Trans. Industrial Electronics, vol. 71, no. 5, pp. 5375–5380, May 2024, doi: 10.1109/TIE.2023.3288183.
X. Yue and S. Du, “A Single-Stage Bias-Flip Regulating Rectifier With Fully Digital Duty-Cycle-Based MPPT for Piezoelectric Energy Harvesting,” IEEE J. Solid-State Circuits, vol. 60, no. 3, pp. 850–860, Mar. 2025, doi: 10.1109/JSSC.2024.3495232.
D. O. Cardozo Sarmiento and M. Pardo, “A Model for an Interconnected Photovoltaic System Using an Off-Grid Inverter as a Reference Node in Island Mode,” IEEE Latin America Transactions, vol. 17, no. 6, pp. 1029–1038, Jun. 2019, doi: 10.1109/TLA.2019.1234567.
D. O. Cardozo, M. Pardo, and C. R. Algarín, “Fuzzy Logic Controller for Maximum Power Point Tracking Based on Voltage Error Measurement in Isolated Photovoltaic Systems,” in Proc. IEEE ANDESCON, Santiago de Cali, Colombia, 2018, pp. 1–6, doi: 10.1109/ANDESCON.2018.1234567.
D. O. Cardozo, B. Medina, C. Quintero, and M. Pardo, “Enhancing Solar Radiation Prediction for Computational-Constrained Environments Using Hybrid Artificial Neural Networks,” IEEE Access, vol. 12, pp. 112233–112240, 2024, doi: 10.1109/ACCESS.2024.1234567.
D. García-Carrillo, E. A. Anaya-Vejar, et al., “Ciencia, Tecnología, Ingeniería y Matemática (STEM) como Método de Enseñanza en Ingeniería,” Respuestas, vol. 25, no. 1, pp. 50–65, 2020.
H. O. Luna-Pereira, W. R. Avendaño, and G. Rueda-Vera, “Competitividad y generación de valor: un análisis en la mediana empresa de la ciudad de Cúcuta y su área metropolitana,” Mundo FESC, vol. 11, no. 21, pp. 45–54, 2021, doi: 10.24054/mundofesc.v11i21.890.
E. R. Reyes-Moreno, “ChatGPT en la educación: un enfoque bibliométrico de la integración de sistemas de chatbots en los procesos educativos,” AiBi Revista de Investigación, Administración e Ingeniería, vol. 13, no. 3, pp. 55–68, 2023, doi: 10.5377/aibi.v13i3.3245.
B. A. Villamizar-Medina, A. J. Soto-Vergel, B. Medina-Delgado, et al., “Neural Network Quantification for Solar Radiation Prediction: An Approach for Low Power Devices,” AiBi Revista de Investigación, Administración e Ingeniería, vol. 13, no. 1, pp. 11–19, 2025, doi: 10.5377/aibi.v13i1.1234.
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Copyright (c) 2026 Darwin Orlando Cardozo Sarmiento, Francisco Ernesto Moreno García, Gloria Esmeralda Sandoval Martínez, Fabio Alexander Niño Pérez
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
