Fuzzy Energy Management in Grid-connected PV Battery System in MATLAB
The given system involves a comprehensive approach to renewable energy generation, storage, and distribution using photovoltaic (PV) panels, batteries, and inverters.
PV Panel and Boost Converter:
Eight PV panels are connected in the series, with two strings, each having a rating of 250 watts.
The total power generated by the PV system is 4,000 watts.
The boost converter is designed based on the power requirements, with equations used to calculate the values of inductance (L) and capacitance (C).
Battery and DC-DC Converter:
The battery is incorporated into the system, and the DC-DC converter is designed to match the power needs.
The battery voltage is set at 240 volts, and the design is based on a power requirement of 5,000 watts.
LCL Filter:
An LCL filter is designed based on the power of the system (5,000 watts), with equations used to calculate the values of inductance (L1, L2) and capacitance (C).
PV Control Algorithm:
The Maximum Power Point Tracking (MPPT) algorithm is employed to maximize power output from the PV panels.
The algorithm considers PV voltage and current as inputs to generate a duty cycle, optimizing power extraction.
Boost Converter Control:
Voltage control is implemented for the boost converter to maintain the desired output voltage.
Load voltage is compared to a reference, and a Proportional-Integral (PI) controller generates a duty cycle for controlling the boost converter.
Inverter Control:
A Fuzzy Logic-based Energy Management System is used for controlling the inverter.
Inputs include PV power and the state of charge (SOC) of the battery, and the system decides whether to draw power from the grid or supply power based on the fuzzy logic rules.
Grid Interaction:
The system can operate in standalone or grid-connected mode.
A control mechanism determines when to draw power from the grid or supply power to it, based on PV and battery conditions.
Conclusion:
The model demonstrates a sophisticated renewable energy system with multiple control algorithms for optimal power generation, storage, and distribution. It considers changing environmental conditions and efficiently manages power flow between various components. The simulation results provide insights into system behavior under different scenarios, emphasizing the importance of intelligent control strategies in renewable energy systems.
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