Demand-side management in Grid-Connected Battery System using Neural Network
 
Experience a powerful AI-driven Demand Side Management (DSM) system built entirely in MATLAB/Simulink, designed for researchers, students, and engineers working on smart grids, battery energy storage systems (BESS), and AI-based energy management. This complete model demonstrates intelligent real-time power coordination between the utility grid, distributed loads, and a grid-connected BESS controlled using a Deep Neural Network (DNN).
 
This professional system showcases:
 
🔧 Smart Grid Architecture Overview
 
⚡ Utility Grid Module
 
154 MW, 34.5 kV primary grid
Stepped down to 400 V for local distribution
Supplies heterogeneous mixed loads across a 24-hour cycle
Provides the benchmark for DSM with and without BESS.
 
🏠 Dynamic Load Modeling (5 Load Categories)
 
Real residential, commercial, and resistive load profiles sampled at 30-minute intervals (48 samples/day):
Load 1 – Residential + Commercial
Load 2 – Residential + Commercial
Load 3 – Resistive + Commercial
Load 4 – Resistive Only
Load 5 – Pure Residential Loads
Each load reflects real-time variation, enabling accurate DSM performance evaluation.
 
🔋 Battery Energy Storage System (BESS)
 
DC-link connected battery subsystem
Bidirectional DC–AC inverter
Smart charging/discharging based on DNN controller output
Smooth SOC transition between 35% and 95%
Supports the grid during peak hours
Charges during off-peak hours
Ensures stable grid operation and optimal energy utilization.
 
🧠 Deep Neural Network Control System (DNN)
 
The core of this product is a trained AI model that analyzes:
Time of Day (0–24 hrs)
Battery State of Charge (SOC)
And intelligently outputs charge/discharge commands:
C > 0 → Discharge (support grid)
C < 0 → Charge (absorb from grid)
C = 0 → Idle
DNN Training Highlights:
10,000 training samples
Train LM Algorithm
Tanh-sigmoid activation
Achieved R = 0.99 regression accuracy
MSE < 0.001
This ensures fast and reliable decision-making for DSM operations.
 
⚡ Performance & DSM Features
 
✔️ Cuts peak grid demand from 160 kW → 120 kW
✔️ Increases load factor from 0.65 → 0.82
✔️ Smooth 24-hour power balance between grid and BESS
✔️ Reduces grid stress during high-load hours
✔️ Automatically charges during off-peak hours
✔️ Ensures efficient daily energy cycling
The system provides measurable improvement in grid stability and performance.
 
📊 What’s Included in the Simulation Model
✔️ Full MATLAB/Simulink DSM Model (.slx)
✔️ Grid subsystem with 154 MW/34.5 kV configuration
✔️ Five real-time dynamic load profiles
✔️ Battery storage with bidirectional inverter
✔️ DNN controller (.mat file + Simulink block)
 
🎯 Ideal For
Smart Grid & AI-Based Energy Management Research
BESS Control & Optimization Projects
DSM Algorithm Development
Grid-Integration Studies
 
▶️ Video Demonstration
🔗 https://www.youtube.com/watch?v=65kiLoFsODw