Grid-Tied PV Battery System with SEPIC Converter in MATLAB Simulink | P&O MPPT & Neural Network EMS

Grid-Tied PV Battery System with SEPIC Converter in MATLAB Simulink

𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧

The 𝐆𝐫𝐢𝐝-𝐓𝐢𝐞𝐝 𝐏𝐕 𝐁𝐚𝐭𝐭𝐞𝐫𝐲 𝐒𝐲𝐬𝐭𝐞𝐦 𝐢𝐧 𝐌𝐀𝐓𝐋𝐀𝐁 is a complete MATLAB/Simulink model designed to study solar PV power generation, battery charging, grid power exchange, and inverter control.

This model uses a 𝐒𝐄𝐏𝐈𝐂 𝐜𝐨𝐧𝐯𝐞𝐫𝐭𝐞𝐫 on the PV side, 𝐏&𝐎 𝐌𝐏𝐏𝐓 for maximum power tracking, and 𝐝𝐪 𝐜𝐨𝐧𝐭𝐫𝐨𝐥 with a neural network energy management logic for grid-side power control.

It is useful for students, researchers, and engineers who want to understand PV battery systems, MPPT control, grid synchronization, and intelligent power sharing in MATLAB/Simulink.

Grid Tied PV battery system in MATLAB

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𝐒𝐲𝐬𝐭𝐞𝐦 𝐎𝐯𝐞𝐫𝐯𝐢𝐞𝐰

The system consists of a PV array, SEPIC converter, DC bus, battery, inverter, LCL filter, and grid connection.

The PV array is connected to the DC bus through the SEPIC converter. The battery is connected on the DC bus side, and the inverter transfers power between the DC bus and the grid.

𝐌𝐚𝐢𝐧 𝐒𝐲𝐬𝐭𝐞𝐦 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞

Section	Details
PV Side	PV array connected through SEPIC converter
DC Side	DC bus with battery connection
Grid Side	Inverter, LCL filter, and grid connection
MPPT Method	P&O MPPT
Inverter Control	dq control logic
Energy Management	Neural network based control using SOC and PV power
Grid	230 V RMS, 50 Hz

𝐏𝐕 𝐀𝐫𝐫𝐚𝐲 𝐃𝐞𝐭𝐚𝐢𝐥𝐬

The PV array is designed using modules connected in series. The transcript mentions a single panel rating of 213.115 W and 8 modules connected in series.

Parameter	Value
Single PV Panel Rating	213.115 W
Open Circuit Voltage	36.3 V
Short Circuit Current	7.84 A
Voltage at Maximum Power Point	29 V
Current at Maximum Power Point	7.35 A
Series Modules	8
Approximate PV Power at 1000 W/m²	Around 1705 W

𝐖𝐨𝐫𝐤𝐢𝐧𝐠 𝐏𝐫𝐨𝐜𝐞𝐬𝐬

The PV array generates DC power based on irradiance. This power is processed through the 𝐒𝐄𝐏𝐈𝐂 𝐜𝐨𝐧𝐯𝐞𝐫𝐭𝐞𝐫 before reaching the DC bus.

The 𝐏&𝐎 𝐌𝐏𝐏𝐓 algorithm measures PV voltage and PV current. Based on the change in power and voltage, it generates the duty cycle for the PWM generator.

The PWM signal controls the converter IGBT, allowing the PV system to track maximum power under changing irradiance conditions.

The battery supports the DC bus and operates in charging mode when sufficient power is available.

The inverter converts DC power into AC power and connects the system to the grid through an LCL filter.

𝐂𝐨𝐧𝐭𝐫𝐨𝐥 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲

The model includes two major control sections:

𝐏&𝐎 𝐌𝐏𝐏𝐓 𝐂𝐨𝐧𝐭𝐫𝐨𝐥

The PV converter is controlled using the 𝐏&𝐎 𝐌𝐏𝐏𝐓 method. It uses PV voltage and PV current as inputs and generates the required duty cycle for maximum power tracking.

𝐝𝐪 𝐈𝐧𝐯𝐞𝐫𝐭𝐞𝐫 𝐂𝐨𝐧𝐭𝐫𝐨𝐥

The inverter uses dq control logic. The reference current is generated using neural network based energy management. This reference current is converted into sinusoidal form and then processed through dq transformation.

The actual inverter current is also converted into dq form. The reference and actual dq currents are compared and processed through a PI controller. Finally, PWM pulses are generated for inverter switching.

𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐄𝐧𝐞𝐫𝐠𝐲 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭

The neural network energy management system uses 𝐒𝐎𝐂 and 𝐏𝐕 𝐩𝐨𝐰𝐞𝐫 as inputs.

It generates the reference current required for inverter control. Based on the generated reference current, power sharing between the battery, PV system, and grid is controlled.

𝐒𝐢𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐓𝐞𝐬𝐭 𝐂𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧

The simulation includes an irradiance variation to test the system response.

Test Condition	Value
Initial Irradiance	1000 W/m²
Changed Irradiance	500 W/m²
Irradiance Change Time	0.3 s
Grid Voltage	230 V RMS
Grid Frequency	50 Hz

𝐒𝐢𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐑𝐞𝐬𝐮𝐥𝐭𝐬

At 1000 W/m², the PV array tracks around 1700 W, which matches the expected PV array power range mentioned in the transcript.

During this condition, the battery operates in charging mode, and the remaining power is exported to the grid.

At 500 W/m², the PV power reduces to around 850 W. The neural network energy management changes the reference current according to the PV power and SOC condition.

Irradiance	Observed PV Power	Operation Mentioned
1000 W/m²	Around 1700 W	Battery charging and power sent to grid
500 W/m²	Around 850 W	Grid supports battery charging based on energy management

The transcript also mentions that the grid voltage and inverter current are in phase during power export. This shows that active power is being transferred to the grid.

𝐊𝐞𝐲 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬

• Complete 𝐌𝐀𝐓𝐋𝐀𝐁/𝐒𝐢𝐦𝐮𝐥𝐢𝐧𝐤 model for grid-tied PV battery system

• 𝐒𝐄𝐏𝐈𝐂 𝐜𝐨𝐧𝐯𝐞𝐫𝐭𝐞𝐫 used for PV-side power conversion

• 𝐏&𝐎 𝐌𝐏𝐏𝐓 algorithm for maximum power tracking

• Battery charging operation through the DC bus

• Inverter connected to grid through 𝐋𝐂𝐋 𝐟𝐢𝐥𝐭𝐞𝐫

• 𝐝𝐪 𝐜𝐨𝐧𝐭𝐫𝐨𝐥 based inverter control

• Neural network energy management using 𝐒𝐎𝐂 and 𝐏𝐕 𝐩𝐨𝐰𝐞𝐫

• Irradiance change tested from 1000 W/m² to 500 W/m²

• Grid voltage and inverter current waveform analysis

• Power exchange between PV, battery, and grid

𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

• Solar PV grid integration study

• PV battery energy management analysis

• MPPT algorithm simulation

• SEPIC converter based renewable energy system design

• Grid-connected inverter control study

• Battery charging and DC bus power sharing analysis

• Neural network based energy management learning

• MATLAB/Simulink based renewable energy system simulation

𝐖𝐡𝐲 𝐓𝐡𝐢𝐬 𝐌𝐨𝐝𝐞𝐥 𝐢𝐬 𝐔𝐬𝐞𝐟𝐮𝐥

This model helps users clearly understand how a PV battery system works with grid connection. It also explains how MPPT, converter control, inverter control, battery charging, and neural network based energy management are integrated in one simulation platform.

The model is especially helpful for learning:

• PV power extraction

• SEPIC converter operation

• P&O MPPT implementation

• Battery charging behavior

• dq inverter control

• Grid power export and import

• Neural network based reference current generation

𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧

The 𝐆𝐫𝐢𝐝-𝐓𝐢𝐞𝐝 𝐏𝐕 𝐁𝐚𝐭𝐭𝐞𝐫𝐲 𝐒𝐲𝐬𝐭𝐞𝐦 𝐢𝐧 𝐌𝐀𝐓𝐋𝐀𝐁 is a complete simulation model for analyzing solar PV power generation, battery support, and grid power exchange.

With 𝐒𝐄𝐏𝐈𝐂 𝐜𝐨𝐧𝐯𝐞𝐫𝐭𝐞𝐫, 𝐏&𝐎 𝐌𝐏𝐏𝐓, 𝐝𝐪 𝐢𝐧𝐯𝐞𝐫𝐭𝐞𝐫 𝐜𝐨𝐧𝐭𝐫𝐨𝐥, and 𝐧𝐞𝐮𝐫𝐚𝐥 𝐧𝐞𝐭𝐰𝐨𝐫𝐤 𝐞𝐧𝐞𝐫𝐠𝐲 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭, this model gives a practical understanding of renewable energy integration in MATLAB/Simulink.