Campus Microgrid Data-Driven Model Identification and Secondary Voltage Control

Eros D. Escobar (), Tatiana Manrique and Idi A. Isaac
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Eros D. Escobar: Research Group on Transmission and Distribution of Electric Power, Universidad Pontificia Bolivariana, Medellín 050031, Antioquia, Colombia
Tatiana Manrique: Mechatronics Engineering Program, Universidad EIA, km 2 + 200 Vía al Aeropuerto JMC, Envigado 055428, Antioquia, Colombia
Idi A. Isaac: Research Group on Transmission and Distribution of Electric Power, Universidad Pontificia Bolivariana, Medellín 050031, Antioquia, Colombia

Energies, 2022, vol. 15, issue 21, 1-19

Abstract: Microgrids deal with challenges presented by intermittent distributed generation, electrical faults and mode transition. To address these issues, to understand their static and dynamic behavior, and to develop control systems, it is necessary to reproduce their stable operation and transient response through mathematical models. This paper presents a data-driven low-order model identification methodology applied to voltage characterization in a photovoltaic system of a real campus microgrid for secondary voltage regulation. First, a literature review is presented focusing on secondary voltage modeling strategies and control. Then, experimental data is used to estimate and validate a low-order MIMO (multiple input–multiple output) model of the microgrid, considering reactive power, solar irradiance, and power demand inputs and the voltage output of the grid node. The obtained model reproduced the real system response with an accuracy of 88.4%. This model is used for dynamical analysis of the microgrid and the development of a secondary voltage control system based on model predictive control (MPC). The MPC strategy uses polytopic invariant sets as terminal sets to guarantee stability. Simulations are carried out to evaluate the controller performance using experimental data from solar irradiance and power demand as the system disturbances. Successful regulation of the secondary voltage output is obtained with a fast response despite the wide range of disturbance values.

Keywords: microgrid; experimental system identification; data-driven model; secondary control; voltage regulation; model predictive control (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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