Neural Network Energy Management in Grid Connected PV Battery System in MATLAB
 
This product provides a complete Neural Network Energy Management in Grid Connected PV Battery System in MATLAB. The model integrates a PV array, boost converter, battery energy storage system, bidirectional DC–DC converter, single-phase inverter, LCL filter, grid connection, and local load. The PV system is controlled using an Incremental Conductance MPPT algorithm to extract maximum power under varying irradiance conditions. The battery converter regulates the DC-link voltage around 400 V and manages charging/discharging based on power balance. An ANN controller receives PV power and battery SOC as inputs and generates the reference current for inverter control, enabling intelligent power exchange between the PV system, battery, load, and grid.
 
The model is suitable for analyzing renewable energy integration, battery power management, grid power exchange, and ANN-based inverter reference current generation in MATLAB/Simulink. It demonstrates how PV power variation, battery SOC, DC bus voltage, grid current, and load/grid power respond under changing solar irradiance conditions.
 
Product Specifications
 
Specification
Details
Product Type
MATLAB/Simulink simulation model
System Type
Grid-connected PV battery energy storage system
Control Method
ANN-based energy management
PV MPPT Method
Incremental Conductance MPPT
PV Configuration
8 series modules and 2 parallel strings
Single PV Module Rating
250 W
Total PV Power
Approximately 4 kW at STC
STC Condition
1000 W/m² and 25°C
PV Voltage Range
Around 240–250 V
Boost Converter Output
Approximately 400 V DC bus
Battery Voltage
Around 240 V
Battery Capacity
48 Ah
Battery Converter
Bidirectional DC–DC converter
DC Bus Voltage Reference
400 V
Inverter Type
Single-phase inverter
Filter Type
LCL filter
Grid Voltage
230 V RMS
Grid Frequency
50 Hz
ANN Inputs
PV power and battery SOC
ANN Output
Reference current
Reference Current Range
Approximately −9 A to +19 A
ANN Structure
Feed-forward neural network
ANN Performance
Regression value around 0.99
Training Error
MSE around 0.009645
Irradiance Test Profile
1000 W/m², 500 W/m², 10 W/m², 500 W/m², 1000 W/m²
Main Outputs
PV voltage/current, battery voltage/current, DC bus voltage, PV power, load power, grid power, battery SOC, grid voltage/current
 
Use Case
 
This product can be used to study and demonstrate intelligent energy management in a grid-connected PV–battery system. It is useful for understanding how an ANN controller can decide the reference current for inverter control based on PV generation and battery SOC.
 
Typical use cases include:
 
Use Case
Description
PV–Battery Energy Management
Analyze how battery charging and discharging are controlled according to PV power availability and load/grid demand.
ANN-Based Control Study
Demonstrate how ANN can be trained using PV power and SOC inputs to generate inverter reference current.
MPPT Performance Analysis
Study Incremental Conductance MPPT performance under changing irradiance conditions.
DC Bus Voltage Regulation
Observe how the bidirectional converter maintains the DC bus voltage near 400 V.
Grid Power Exchange Analysis
Analyze when the system sends power to the grid or takes power from the grid.
Battery SOC Behavior Study
Evaluate battery charging/discharging response under variable solar irradiation.
MATLAB/Simulink Learning
Useful for students, researchers, and engineers learning PV–battery grid-connected system modeling.
Renewable Energy Research
Can be used as a base model for research related to smart grids, microgrids, EMS, ANN control, and battery-integrated PV systems.