Fuzzy Energy Management in Grid-connected PV Battery System in MATLAB
- lms editor
- Nov 13, 2025
- 3 min read
🌞 Introduction
The Fuzzy Energy Management System (FEMS) for a grid-connected photovoltaic (PV) and battery system represents a smart approach to optimize renewable energy utilization, storage, and distribution. Implemented in MATLAB/Simulink, this system ensures stable operation, intelligent decision-making, and efficient power exchange between PV arrays, battery storage, and the utility grid.
By combining Maximum Power Point Tracking (MPPT), boost converter voltage regulation, and fuzzy logic-based inverter control, the system dynamically responds to changing solar irradiance and load conditions — maintaining balance between generation, storage, and grid interaction.
⚙️ PV Panel and Boost Converter
The PV subsystem consists of eight panels connected in series, forming two parallel strings, with each panel rated at 250 W.
The total PV generation capacity is 4,000 W (4 kW) under standard irradiance conditions.
A boost converter steps up the PV voltage to the required DC bus level.
The boost converter ensures a stable DC link and maintains the PV operation at maximum efficiency using MPPT control.
🔋 Battery Energy Storage and DC–DC Converter
The battery bank acts as the energy buffer, storing excess solar power and supplying power during low irradiance or peak demand.
The nominal voltage of the battery is 240 V, and the design supports a power capacity of 5,000 W.
A bidirectional DC–DC converter manages charge and discharge modes according to energy demand and PV availability.
Charging mode: Activated when PV generation > load demand.
Discharging mode: Activated when PV generation < load demand or during grid outages.
This ensures seamless power balance and improved battery longevity.
⚡ LCL Filter Design
To ensure high-quality power at the inverter output, an LCL filter is implemented.
Designed for 5 kW system capacity, it consists of two inductors (L₁ and L₂) and a capacitor (C).
The filter minimizes harmonic distortion (THD) and ensures smooth grid current injection.
🧠 PV Control Algorithm (MPPT)
To maximize the energy output, the Incremental Conductance (INC) MPPT algorithm is implemented.
Inputs: PV voltage (Vpv) and current (Ipv).
Output: Duty cycle (D) to control the boost converter.
The algorithm continuously adjusts the operating point of the PV panels to ensure maximum power extraction under varying irradiance levels.
This enhances overall system efficiency and improves DC bus stability.
⚙️ Boost Converter Control
The boost converter uses voltage control with a Proportional-Integral (PI) controller:
The load voltage is compared against a reference voltage, and the error is processed through the PI controller.
The controller output generates the duty cycle that adjusts the converter switch, ensuring the DC output voltage remains constant even under changing PV conditions.
This provides a smooth DC link for the inverter and battery interface.
🤖 Fuzzy Logic-Based Inverter Control
The inverter control employs a Fuzzy Logic Controller (FLC) within the Energy Management System (EMS).
Inputs:
PV power (Ppv)
Battery state of charge (SOC)
Outputs:
Control signal for inverter operation
Decision on grid power exchange
The fuzzy rule base determines:
When to feed power into the grid,
When to charge or discharge the battery, and
When to draw power from the grid to maintain system balance.
Fuzzy logic enhances decision precision under uncertainties such as fluctuating solar irradiance or dynamic load variations.
🔌 Grid Interaction and Power Flow
The hybrid system operates in two modes:
Standalone Mode: The system powers local loads using PV and battery energy.
Grid-Connected Mode:
When PV power is surplus, the inverter supplies excess energy to the grid.
When PV generation is insufficient, the inverter draws power from the grid.
This bidirectional power flow enables energy trading, load sharing, and stability support for both microgrid and main grid operations.
📊 Simulation Insights
The MATLAB simulation results show:
Stable DC bus voltage regulation even under variable irradiance.
Dynamic power sharing between PV, battery, and grid based on fuzzy logic decisions.
Reduced THD in inverter current due to the LCL filter.
Efficient MPPT tracking ensuring PV modules operate at optimal power levels.
🏁 Conclusion
The Fuzzy Energy Management System (FEMS) effectively coordinates PV generation, battery storage, and grid interaction in a MATLAB/Simulink-based grid-connected microgrid.
Through MPPT, PI-based converter control, and fuzzy logic intelligence, the system ensures:
Efficient energy utilization
Smooth grid integration
Low harmonic distortion
Reliable operation under variable environmental conditions
This approach highlights the importance of AI-driven control strategies in achieving sustainable and smart energy management in modern renewable power systems.
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