Intelligent Grid-Connected Photovoltaic Energy Management through Hybrid Storage Regulation

Abstract

The increasing penetration of solar photovoltaic (PV) generation into modern distribution networks introduces significant power-quality and stability challenges, primarily due to the intermittent nature of solar irradiance and sudden load disturbances. Conventional proportional–integral (PI) controlled PV systems supported only by battery storage respond slowly to fast transients, resulting in deep voltage sags, large voltage swells, prolonged settling times, and high total harmonic distortion (THD). This paper proposes a Model Predictive Control (MPC) based energy-management scheme for a grid-connected hybrid PV system that combines a battery energy storage system (BESS) with a supercapacitor energy storage system (SCESS) on a common hybrid DC bus. The MPC controller receives the DC-link voltage reference, measured DC-link voltage, grid current, load power, battery and supercapacitor states of charge, and a predictive disturbance signal derived from irradiance, load, and grid-voltage sensors; it generates optimal gate signals for the boost converter and the three-phase inverter together with battery and supercapacitor power references. The complete system is modeled and simulated in MATLAB/Simulink and benchmarked against an existing PI-controlled PV–battery system. Simulation results demonstrate that the proposed MPC scheme limits the voltage sag to 400 V compared with 250 V for the PI system, restricts the voltage swell to 405 V against 550 V, achieves near-instantaneous settling to the 400 V steady state, and reduces the output-voltage THD from 8.5% to 2.1%, comfortably satisfying the IEEE Std. 519 limit of 5%. The frequency-decoupled power sharing between the battery and the supercapacitor further reduces battery stress and improves the overall reliability and lifetime of the storage system.

Introduction

The study proposes and evaluates a Model Predictive Control (MPC)-based hybrid energy storage system (HESS) for a grid-connected solar photovoltaic (PV) system. The system combines a battery and a supercapacitor to improve power quality, enhance energy management, and overcome the limitations of conventional proportional–integral (PI) controllers.

As solar PV output depends on changing irradiance and temperature, fluctuations in generation can cause voltage sags, swells, oscillations, and harmonic distortion at the grid connection point. Batteries can supply energy over long durations but respond slowly to rapid power fluctuations, while supercapacitors respond almost instantaneously but store limited energy. A hybrid energy storage system exploits these complementary characteristics by assigning slow, average power demands to the battery and fast transient power demands to the supercapacitor.

Unlike traditional PI controllers, which react only after errors occur and cannot explicitly handle system constraints, MPC predicts future system behavior using a mathematical model and computes the optimal control action while satisfying converter, current, and state-of-charge constraints. The proposed controller simultaneously regulates the DC-link voltage, controls converter and inverter switching, allocates battery and supercapacitor power, and incorporates predictive disturbance sensing based on irradiance, load, and grid-voltage measurements.

The system architecture consists of eleven components, including the PV array, MPPT controller, DC–DC boost converter, hybrid DC bus, battery, supercapacitor, inverter, utility grid, three-phase load, MPC controller, and predictive disturbance sensing layer. Mathematical models are developed for the PV array, boost converter, battery, supercapacitor, inverter, and grid. The continuous-time models are discretized and used within the MPC framework to minimize a cost function that balances DC-link voltage regulation, current tracking, battery stress reduction, supercapacitor utilization, and switching effort while satisfying operational constraints.

A conventional PI-controlled PV–battery system is implemented as a benchmark under identical operating conditions. Both systems are simulated in MATLAB/Simulink using a 1000 W PV array, a 400 V DC-link, and disturbance scenarios involving sudden reductions in solar irradiance and load changes.

Simulation results demonstrate the superiority of the proposed MPC-based hybrid system. During voltage sag events, the conventional PI controller allows the DC-link voltage to fall from 400 V to 250 V, whereas the MPC maintains the voltage close to its nominal value by rapidly dispatching supercapacitor power. During voltage swell conditions, the PI controller produces an overvoltage of 550 V, while the MPC limits the peak to approximately 405 V, reducing overvoltage stress by about 97%. The predictive disturbance sensing layer enables the controller to react before large deviations occur, leading to faster settling, improved voltage stability, lower harmonic distortion, and more effective coordination between the battery and supercapacitor.

Conclusion

This paper developed and validated a model predictive control based energy-management and power-quality scheme for a grid-connected solar PV system equipped with hybrid battery–supercapacitor storage. A unified MPC layer generates the boost-converter and inverter gate signals and the storage power references from DC-link, grid, load, SOC, and predictive disturbance measurements. Simulink-based evaluation against an identically rated PI-controlled PV–battery baseline demonstrated: elimination of the 250 V voltage sag (MPC holds 400 V), suppression of the 550 V swell to 405 V, near-instantaneous settling to the 400 V steady state, reduction of THD from 8.5% to 2.1% in compliance with IEEE Std. 519, smooth overshoot-free load power delivery, and effective frequency-decoupled power sharing that confines transients to the supercapacitor and average power to the battery. Collectively these results establish MPC with hybrid storage as a decisively superior alternative to conventional PI control for high-penetration PV integration. Future work will proceed along four directions: (i) real-time hardware-in-the-loop and laboratory-prototype validation of the controller on a DSP/FPGA platform; (ii) incorporation of machine-learning based irradiance and load forecasting into the predictive disturbance layer to lengthen the effective prediction horizon; (iii) extension of the cost function with explicit battery-degradation and thermal models for lifetime-aware dispatch; and (iv) scaling of the architecture to multi-inverter microgrids with distributed MPC coordination and islanding capability.

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Copyright

Copyright © 2026 Ch. Vineeth, Dr. T. Sudhakar Babu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.