Fuzzy Logic-Driven Power Management Algorithm (PMA) for Grid-Tied PV System with Hybrid Energy Storage

Abstract

This project proposes a fuzzy logic-based power management algorithm (PMA) for integrating grid-tied photovoltaic (PV) systems with a hybrid energy storage. The proposed algorithm’s stated goals include improving the grid\'s stability and dependability, and making better utilization of renewable energy. The suggested control scheme allows for efficient regulation of power flow and optimizes self-consumption for PV energy through successfully coordinating the photovoltaic system, grid, with hybrid energy storage. Aiming to maximize power management, the fuzzy logic controller takes into account an assortment of input factors, including PV output, energy storage state-of-charge, and grid demand. These scheme\'s efficacy in integrating renewable energy sources, decreasing grid reliance, and guaranteeing a steady power supply is shown by the simulation results.

Introduction

Context and Importance:
With growing emphasis on reducing energy use and adopting green energy, modern power systems prioritize eco-friendly technologies. Photovoltaic (PV) systems paired with wind turbines are among the greenest solutions. PV is favored for being cost-effective, efficient, maintenance-free, and consistent. However, PV output fluctuates due to environmental factors like temperature, sunlight variability, shading, and humidity, impacting power quality and system longevity.

Role of Hybrid Energy Storage Systems (HESS):
To stabilize PV systems, microgrids integrate energy storage devices—primarily batteries and super-capacitors. Batteries offer high energy density but slow charge/discharge rates, while super-capacitors provide fast response but store less energy. Combining them creates a hybrid energy storage system (HESS) that extends battery life and ensures stable power supply.

System Framework:

• Main components: PV array, quadratic boost converter, battery, super-capacitor, LC filter, DC-DC converters, voltage source converter (VSC), RL and nonlinear loads.

• PV arrays convert solar energy to DC power, which fluctuates due to solar radiation changes.

• A quadratic boost converter raises PV voltage to a stable DC link voltage.

• The VSC converts DC to AC for grid integration, with an LC filter smoothing output waveforms.

• Battery stores energy for long-term supply; super-capacitor handles fast transient power fluctuations. Both connect via bi-directional DC-DC converters managing charging/discharging.

Power Control and Management:

• A fuzzy logic controller stabilizes the DC link voltage by adjusting system controls dynamically based on voltage errors.

• A power management algorithm (PMA) determines power flow priorities considering battery and super-capacitor states of charge (SOC), available PV power, and grid demand.

• PMA operates in three modes:

  1. Insufficient Power Mode (IPM): Power demand exceeds PV output; grid, battery, and super-capacitor compensate.

  2. Sufficient Power Mode (SPM): PV output exceeds demand; surplus charges storage devices or feeds back to grid.

  3. Floating Power Mode (FPM): PV power matches demand; storage may charge from grid if needed; super-capacitor handles transients.

• PI controllers regulate power flow to battery, super-capacitor, PV, and grid components, with PWM signals controlling converters.

Power Balance and Stability:

• Power equilibrium ensures load demands are met by the combination of PV, battery, super-capacitor, and grid power.

• Total power splits into average power (steady supply) and transient power (rapid fluctuations), managed between battery and super-capacitor.

• Voltage controllers maintain DC link voltage stability and regulate current flow.

Simulation Results:

• Under varying PV power, the system maintains stable DC link voltage (~100 V), with battery discharging slightly and super-capacitor voltage stable, providing transient support.

• Grid current remains stable, and total harmonic distortion (THD) is low (~0.41%), indicating high power quality.

• When PV power drops, battery and super-capacitor collaborate to stabilize supply effectively.

• Under load variation, the system also maintains stable voltage and power flows, with storage and grid adapting dynamically to keep balance.

Conclusion

In conclusion, the fuzzy logic-based power management algorithm with a hybrid energy storage provides an effective solution for challenges associated with grid-tied photovoltaic (PV) integration and energy storage. By combining PV generation with energy storage, the system optimizes power flow, enhancing overall performance and reliability. The fuzzy logic controller enables real-time adaptive control of the energy storage system\'s charging and discharging, efficiently handling imprecise and dynamic conditions. As a result, the system improves grid stability, reduces energy costs, and increases resilience by ensuring optimal utilization of available storage capacity and renewable energy sources. Integrating PV with energy storage offers multiple advantages. Excess energy generated during peak PV output can be stored, reducing reliance on the grid and minimizing energy waste. Conversely, stored energy can be discharged when PV generation is low or demand is high, ensuring a reliable and uninterrupted power supply.

References

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Copyright

Copyright © 2025 U. Sreenivas, K. Ashrafulla Khan, M. Jyothsna, G. Chandrika, G. Govardhan Reddy, S. Harshitha. 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.