Fuzzy Energy Management in Grid Connected PV Battery System in MATLAB

Fuzzy Energy Management in Grid Connected PV Battery System in MATLAB

𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧

A grid-connected solar PV system with battery energy storage requires an intelligent control method to manage power flow between the PV array, battery, DC bus, AC load, and utility grid.

Fuzzy Energy Management in Grid Connected PV Battery System

Fuzzy Energy Management in grid connected pv battery system in matlab

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This MATLAB/Simulink model uses a Fuzzy Logic Energy Management System to determine the inverter current reference according to:

• Solar PV power availability

• Battery state of charge

• Power transfer requirement

• Grid import and export conditions

The complete system also includes:

• Incremental Conductance MPPT

• Boost converter

• Bidirectional battery DC–DC converter

• 400 V DC-link regulation

• Grid-connected full-bridge inverter

• LCL filter

• PLL-based synchronization

• d-q current control

• Fuzzy Logic Controller

• Grid import/export operation

The model is useful for students, researchers, and engineers who want to understand intelligent energy management in grid-connected renewable energy systems using MATLAB/Simulink.

𝐒𝐲𝐬𝐭𝐞𝐦 𝐎𝐯𝐞𝐫𝐯𝐢𝐞𝐰

The overall system consists of five major sections:

1. Solar PV generation system

2. PV boost converter with MPPT

3. Battery energy storage with bidirectional converter

4. Fuzzy energy management controller

5. Grid-connected inverter with LCL filter

Main Power Flow

Solar PV → Boost Converter → DC Bus → Grid Inverter → AC Load/Grid

The battery is connected directly to the common DC bus through a bidirectional DC–DC converter.

Depending on PV generation and battery SOC, the system can:

• Supply solar power to the AC side

• Charge the battery

• Discharge the battery

• Export excess power to the grid

• Import power from the grid

• Maintain the DC-link voltage

𝐏𝐕 𝐀𝐫𝐫𝐚𝐲 𝐂𝐨𝐧𝐟𝐢𝐠𝐮𝐫𝐚𝐭𝐢𝐨𝐧

The solar PV source uses eight modules in series per string and two parallel strings.

Parameter	Value
Power rating of one PV module	250 W
Voltage near maximum power point per module	30.7 V
Modules connected in series	8
Parallel strings	2
Power per string	2000 W
Total PV rated power	4000 W
PV string operating voltage	≈245.6 V
DC bus voltage	400 V

Therefore, the PV system is designed to generate approximately 4 kW under rated irradiance conditions.

𝐏𝐕 𝐁𝐨𝐨𝐬𝐭 𝐂𝐨𝐧𝐯𝐞𝐫𝐭𝐞𝐫

The PV voltage is lower than the required DC-link voltage. Therefore, a boost converter is used between the PV array and the DC bus.

Boost Converter Specifications

Parameter	Value
PV rated power	4000 W
Input voltage	≈245.6 V
Required DC output	400 V
Switching frequency	10 kHz
Converter type	DC–DC Boost Converter

The converter inductor and capacitor are selected based on these operating requirements.

The calculated component values are then implemented directly in the Simulink boost converter.

𝐈𝐧𝐜𝐫𝐞𝐦𝐞𝐧𝐭𝐚𝐥 𝐂𝐨𝐧𝐝𝐮𝐜𝐭𝐚𝐧𝐜𝐞 𝐌𝐏𝐏𝐓

The solar PV system uses an Incremental Conductance MPPT algorithm.

Inputs

• PV voltage

• PV current

Output

• Duty cycle for the boost converter

The controller continuously monitors PV voltage and current and adjusts the duty cycle to operate the PV array close to its maximum power point.

MPPT Control Flow

PV Voltage + PV Current → Incremental Conductance MPPT → Duty Cycle → PWM Generator → Boost Converter

This allows the PV array to extract maximum available power even when solar irradiance changes.

𝐁𝐚𝐭𝐭𝐞𝐫𝐲 𝐄𝐧𝐞𝐫𝐠𝐲 𝐒𝐭𝐨𝐫𝐚𝐠𝐞

The battery is connected to the 400 V DC bus through a bidirectional DC–DC converter.

Parameter	Value
Battery voltage	240 V
DC bus voltage	400 V
Converter power design level	Based on system power requirement
Switching frequency	10 kHz
Power flow	Bidirectional

The bidirectional converter supports:

• Battery charging

• Battery discharging

• DC-link voltage support

• Grid-assisted charging

𝐁𝐚𝐭𝐭𝐞𝐫𝐲 𝐂𝐨𝐧𝐯𝐞𝐫𝐭𝐞𝐫 𝐕𝐨𝐥𝐭𝐚𝐠𝐞 𝐂𝐨𝐧𝐭𝐫𝐨𝐥

The battery converter is controlled using the DC-link voltage.

Control Sequence

DC Bus Voltage → Compare with 400 V → Voltage Error → PI Controller → Duty Cycle → PWM Generator → Bidirectional Converter

The reference DC voltage is:

400 V

If the DC-link voltage decreases, the battery or grid can supply additional power.

If the DC-link has excess energy, the battery can absorb power depending on the energy management command.

The primary objective is to keep the DC bus close to 400 V under changing power conditions.

𝐆𝐫𝐢𝐝-𝐂𝐨𝐧𝐧𝐞𝐜𝐭𝐞𝐝 𝐈𝐧𝐯𝐞𝐫𝐭𝐞𝐫

A full-bridge inverter connects the DC bus to the AC system.

The inverter is designed for the combined PV and battery power requirement.

Parameter	Value
PV power	4 kW
Battery contribution considered	1 kW
Inverter design power	5 kW
DC-link voltage	400 V
Grid voltage	230 V
Grid frequency	50 Hz
Inverter switching frequency	7.5 kHz
Output filter	LCL filter

The LCL filter reduces switching-frequency harmonics before the inverter current reaches the point of common coupling.

𝐋𝐂𝐋 𝐅𝐢𝐥𝐭𝐞𝐫

The inverter output uses an LCL filter consisting of:

• Inverter-side inductor

• Filter capacitor

• Grid-side inductor

The filter components are selected considering:

• 5 kW inverter power

• 400 V DC bus

• 230 V AC grid

• 50 Hz fundamental frequency

• 7.5 kHz switching frequency

The LCL filter helps obtain a clean sinusoidal grid current with reduced high-frequency ripple.

𝐅𝐮𝐳𝐳𝐲 𝐄𝐧𝐞𝐫𝐠𝐲 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭

The main intelligent part of the model is the Fuzzy Logic Energy Management System.

The fuzzy controller decides how much inverter current should flow and in which direction.

Fuzzy Controller Inputs

1. PV power

2. Battery SOC

Fuzzy Controller Output

• Inverter current reference

𝐏𝐞𝐫-𝐔𝐧𝐢𝐭 𝐈𝐧𝐩𝐮𝐭 𝐒𝐜𝐚𝐥𝐢𝐧𝐠

Before applying the signals to the fuzzy controller, they are normalized.

Signal	Actual Range	Fuzzy Range
PV power	0–4000 W	0–1 pu
Battery SOC	0–100%	0–1 pu
Current reference	—	−1.3 to +1.3

This normalization makes fuzzy membership-function design easier and improves controller implementation.

𝐅𝐮𝐳𝐳𝐲 𝐌𝐞𝐦𝐛𝐞𝐫𝐬𝐡𝐢𝐩 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬

PV Power Membership Functions

PV power is divided into approximately four operating levels:

• Zero

• Small

• Medium

• Big

Battery SOC Membership Functions

Battery SOC is divided into five levels:

• Zero

• Small

• Medium

• Big

• Very Big

Output Current Reference

The fuzzy output represents the required direction and magnitude of power transfer.

Typical linguistic levels include:

• Negative Big

• Negative Medium

• Zero

• Positive Medium

• Positive Big

𝐌𝐞𝐚𝐧𝐢𝐧𝐠 𝐨𝐟 𝐭𝐡𝐞 𝐅𝐮𝐳𝐳𝐲 𝐎𝐮𝐭𝐩𝐮𝐭

The sign of the current reference is important.

Fuzzy Output	Power Flow Meaning
Large positive	Strong power transfer from DC side toward AC/grid side
Small positive	Moderate export from DC side
Near zero	Nearly balanced operating condition
Small negative	Moderate power import from grid
Large negative	Strong grid power import

For example:

• High PV + High SOC → Positive current reference

• Low PV + Low SOC → Negative current reference

• Medium PV + Medium SOC → Current reference near zero

This allows the system to automatically select grid import or export operation.

𝐄𝐱𝐚𝐦𝐩𝐥𝐞 𝐅𝐮𝐳𝐳𝐲 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐋𝐨𝐠𝐢𝐜

PV Condition	Battery SOC	Current Command	Typical Operation
Very low	Very low	Large negative	Import power from grid
Low	Medium	Negative	Grid-assisted supply/charging
Medium	Medium	Near zero	Balanced operation
High	High	Positive	Export available power
Very high	Very high	Large positive	Maximum grid-side power transfer

The exact current reference varies continuously according to the fuzzy rule surface.

𝐈𝐧𝐯𝐞𝐫𝐭𝐞𝐫 𝐂𝐮𝐫𝐫𝐞𝐧𝐭 𝐑𝐞𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧

The fuzzy controller produces a DC-valued reference quantity.

This signal is scaled and combined with a synchronized sinusoidal waveform to produce an AC current reference.

Reference Generation Process

PV Power + SOC → Fuzzy Controller → Current Magnitude → Sinusoidal Reference → d-q Transformation

The resulting d-axis and q-axis current references are then used by the inverter current controller.

𝐏𝐋𝐋 𝐚𝐧𝐝 𝐆𝐫𝐢𝐝 𝐒𝐲𝐧𝐜𝐡𝐫𝐨𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧

The measured grid voltage is supplied to a Phase-Locked Loop.

The PLL provides:

• Grid angle

• Sine reference

• Cosine reference

• Synchronization information

This ensures that the inverter current remains synchronized with the utility voltage.

The system operates at approximately:

50 Hz

𝐝-𝐪 𝐂𝐮𝐫𝐫𝐞𝐧𝐭 𝐂𝐨𝐧𝐭𝐫𝐨𝐥

The measured grid current is converted into the d-q reference frame.

Control Process

1. Measure grid current.

2. Convert current into d-q components.

3. Compare measured d-q currents with reference currents.

4. Process errors through current controllers.

5. Generate d-q control signals.

6. Convert the commands back to the stationary frame.

7. Generate PWM pulses.

8. Control the inverter switches.

This closed-loop structure allows bidirectional inverter current control.

𝐖𝐨𝐫𝐤𝐢𝐧𝐠 𝐏𝐫𝐨𝐜𝐞𝐬𝐬

The complete operation can be understood in the following sequence:

1. PV panels generate DC power according to solar irradiance.

2. Incremental Conductance MPPT calculates the required boost duty cycle.

3. The boost converter raises the PV voltage to the 400 V DC bus.

4. The battery bidirectional converter supports DC-link regulation.

5. PV power and battery SOC are normalized.

6. The normalized signals are supplied to the fuzzy controller.

7. Fuzzy logic generates the inverter current reference.

8. The PLL synchronizes the control system with the grid.

9. The current controller regulates the inverter current.

10. The LCL filter removes high-frequency switching components.

11. Power is exchanged between the PV-battery system and the grid according to operating conditions.

𝐈𝐫𝐫𝐚𝐝𝐢𝐚𝐧𝐜𝐞 𝐕𝐚𝐫𝐢𝐚𝐭𝐢𝐨𝐧

The simulation tests the PV system under changing irradiance.

A representative test pattern is:

Irradiance	Approximate PV Output
1000 W/m²	4000 W
500 W/m²	2000 W
Very low irradiance	Close to 0 W
500 W/m²	2000 W
1000 W/m²	4000 W

This test demonstrates whether the MPPT, battery controller, and fuzzy energy management system can respond correctly to rapidly changing PV generation.

𝐁𝐚𝐭𝐭𝐞𝐫𝐲 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐔𝐧𝐝𝐞𝐫 𝐕𝐚𝐫𝐲𝐢𝐧𝐠 𝐏𝐕 𝐏𝐨𝐰𝐞𝐫

The battery changes between charging and discharging depending on PV availability and the fuzzy control decision.

Approx. Simulation Interval	PV Condition	Battery Behavior
0–0.6 s	PV available	Charging
0.6–0.9 s	PV close to zero	Discharging
0.9–1.5 s	PV power returns	Charging

When PV generation decreases significantly, stored battery energy can support the system.

When sufficient PV energy becomes available, the battery can return to charging operation.

𝐋𝐨𝐰 𝐁𝐚𝐭𝐭𝐞𝐫𝐲 𝐒𝐎𝐂 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧

An additional operating condition is tested with the battery SOC set close to 10%.

Under this condition:

• The fuzzy controller detects low battery energy.

• The inverter current reference becomes negative.

• Power is imported from the utility grid.

• The battery operates mainly in charging mode.

• SOC gradually increases.

• Grid support continues even when PV generation becomes very low.

This demonstrates one of the main advantages of fuzzy energy management: automatic power-flow decision making without manually changing operating modes.

𝐆𝐫𝐢𝐝 𝐈𝐦𝐩𝐨𝐫𝐭 𝐚𝐧𝐝 𝐄𝐱𝐩𝐨𝐫𝐭

The voltage-current phase relationship indicates the direction of active power transfer.

Export Condition

When sufficient PV and battery energy are available:

• Current flows from the inverter toward the AC side.

• Power can be supplied to the AC load and grid.

• Grid voltage and current follow the corresponding export relationship.

Import Condition

When PV power and battery SOC are low:

• Power is obtained from the utility grid.

• Grid current direction reverses.

• A phase reversal can be observed between the measured voltage and current according to the selected current sign convention.

• Imported power can support the DC bus and battery charging.

𝐒𝐢𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐑𝐞𝐬𝐮𝐥𝐭𝐬

The model monitors several important electrical quantities.

PV Measurements

• PV voltage

• PV current

• PV power

PV output follows the applied solar irradiance changes and reaches approximately 4 kW at 1000 W/m².

DC-Link Measurements

• DC-link voltage

• Converter response

• Voltage settling

After the initial transient, the voltage-control system maintains the DC bus near 400 V.

Battery Measurements

• Battery voltage

• Battery current

• Battery SOC

• Charging/discharging status

The battery automatically changes its power direction according to PV generation and energy management requirements.

Grid Measurements

• Grid voltage

• Grid current

• Active power

• Power-flow direction

The inverter produces sinusoidal grid current and supports both power import and export.

Frequency

The system frequency remains close to:

50 Hz

even when PV irradiance and battery operating conditions change.

𝐌𝐚𝐢𝐧 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐞𝐝 𝐒𝐢𝐠𝐧𝐚𝐥𝐬

Scope/Measurement	Signals
PV scope	PV voltage, current, power
DC bus scope	DC-link voltage
Battery scope	Battery voltage and current
Energy scope	PV power, load power, SOC, grid power
Grid scope	Grid voltage and current
PCC measurement	Point-of-common-coupling variables
Frequency scope	Grid frequency

𝐊𝐞𝐲 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬

• 4 kW solar PV array

• Eight PV modules in series per string

• Two parallel PV strings

• Incremental Conductance MPPT

• 400 V regulated DC bus

• Battery energy storage

• Bidirectional battery DC–DC converter

• Fuzzy Logic Energy Management System

• PV power and SOC based decision making

• Grid import and export operation

• Full-bridge grid-connected inverter

• LCL output filter

• PLL synchronization

• d-q current control

• Bidirectional active-power flow

• Dynamic irradiance testing

• Low-SOC grid charging operation

• 50 Hz grid-frequency operation

𝐖𝐡𝐲 𝐔𝐬𝐞 𝐅𝐮𝐳𝐳𝐲 𝐋𝐨𝐠𝐢𝐜 𝐟𝐨𝐫 𝐄𝐧𝐞𝐫𝐠𝐲 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭?

Fuzzy logic is suitable for hybrid renewable energy systems because the controller can make decisions based on operating ranges rather than relying only on fixed switching thresholds.

Major advantages include:

• Easy implementation of expert control rules

• Smooth transition between operating conditions

• Suitable for nonlinear renewable energy systems

• Handles fluctuating PV generation

• Handles varying battery SOC

• Supports automatic grid import/export decisions

• Reduces the need for multiple manually selected operating modes

𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

This type of MATLAB/Simulink model is useful for studying:

• Grid-connected solar PV systems

• PV-battery energy management

• Battery charging and discharging control

• Smart inverter control

• Renewable energy integration

• Grid-supporting battery systems

• Bidirectional power conversion

• Intelligent microgrid control

• Fuzzy Logic Controller development

• MPPT controller analysis

• Grid current regulation

• Energy storage integration

𝐖𝐡𝐚𝐭 𝐂𝐚𝐧 𝐁𝐞 𝐋𝐞𝐚𝐫𝐧𝐞𝐝 𝐟𝐫𝐨𝐦 𝐓𝐡𝐢𝐬 𝐌𝐨𝐝𝐞𝐥?

By studying the complete simulation, users can understand:

• How to size a PV boost converter

• How a bidirectional battery converter operates

• How a 400 V DC bus is regulated

• How Incremental Conductance MPPT works in Simulink

• How PV power and SOC are normalized

• How fuzzy membership functions are selected

• How fuzzy rules determine grid current

• How a PLL synchronizes an inverter with the grid

• How d-q current controllers regulate inverter output

• How an LCL filter interfaces the inverter with the AC grid

• How grid power direction changes during charging and discharging

• How battery SOC influences energy management

𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧

The Fuzzy Energy Management in Grid Connected PV Battery System in MATLAB demonstrates a complete intelligent renewable-energy power management structure.

A 4 kW PV array is connected to a 400 V DC bus through an Incremental Conductance MPPT controlled boost converter. A battery energy storage system is integrated through a bidirectional DC–DC converter, while a full-bridge inverter connects the DC system to the 230 V, 50 Hz grid.

The fuzzy energy management controller uses PV power and battery SOC to determine the inverter current reference. As a result, the system can automatically change between grid import, balanced operation, battery support, and grid export.

The simulation clearly demonstrates MPPT control, battery charging and discharging, DC-link regulation, intelligent energy management, grid synchronization, and bidirectional power flow within a single MATLAB/Simulink environment.