Demand Response of EV in IEEE 33 Bus Using PSO | Minimizing Losses, Peak Load & Costs

Demand Response of EV in IEEE 33 Bus Using PSO | Minimizing Losses, Peak Load & Costs

In this video, we explore Demand Response (DR) for Electric Vehicles (EVs) in the IEEE 33 Bus System using Particle Swarm Optimization (PSO). We optimize EV charging/discharging to minimize power losses, reduce peak load, and lower system & customer costs while considering key constraints like SOC, line limits, and EV locations.

Project Specification

Title: Demand Response OF EV in IEEE 33 Bus using PSO.

Test Network: IEEE 33 Bus.

Optimization Algorithm: Particle Swarm Optimization (PSO)

Optimization Variables:

• Power Charging/Discharging from EV.

Objective Function:

1. Minimize losses.
2. Peak Load.
3. System & EV Costumer Cost.

Constraints of Function:

1. Line Limits.
2. Location Of EV.
3. Soc of charge for EV.
4. Ability of Charging/Discharge of EV.

Input Parameters:

1. EV Parameters (Zhang, L., & Zhang, Y. (2020). "Impact of EV integration on power system operation: A review of approaches and challenges).

• SOC Thresholds= 20% to 80% SOC.
• Battery Capacity= 60 kWh.
• Energy Consumption= 180 Wh/km
• Charging Efficiency= 90%
• Discharging Efficiency= 90%
• V2G Power= 7 kW.

Scenarios:

1. Base Scenarios:

Without Demand response for EVs.

2. Optimization Scenarios:

With Demand response for EVs.

Results and graph:

1. Power Loss.
2. Voltage Profile of System.
3. Total system cost/customer charging Cost.
4. Load profile.
5. Peak Load.
6. EV Profile.

### 🔗 Useful Links & Resources: ###

http://simulationtutor.com/

📌 Tags & Keywords: #matlabprojects #matlabcode #electricalengineering

The End!