Improved approach for electric vehicle rapid charging station placement and sizing using Google maps and binary...

Area of Science:

• Electrical Engineering
• Environmental Science
• Optimization Techniques

Background:

• Electric vehicles (EVs) are crucial for mitigating global warming and pollution.
• Efficient recharging infrastructure is essential for widespread EV adoption.
• Existing rapid charging station (RCS) planning methods often overlook specific energy loss components.

Purpose of the Study:

• To develop a novel approach for optimal rapid charging station (RCS) planning.
• To incorporate transportation loss, buildup, substation energy loss, and harmonic power loss into the planning model.
• To introduce and evaluate a new optimization technique, the binary lightning search algorithm (BLSA).

Main Methods:

• A novel optimization technique, the binary lightning search algorithm (BLSA), was developed.
• BLSA was applied to optimal RCS planning, considering various cost factors including harmonic power loss.
• The proposed method was compared against a conventional RCS planning method using the IEEE 34-bus test system.

Main Results:

• The binary lightning search algorithm (BLSA) demonstrated superior performance compared to other optimization techniques.
• The proposed RCS planning method, utilizing BLSA, achieved a 10% reduction in daily total cost.
• The inclusion of harmonic power loss in the planning model led to more comprehensive and cost-effective solutions.

Conclusions:

• The proposed BLSA-based RCS planning approach is more effective than conventional methods.
• Integrating harmonic power loss significantly improves the economic efficiency of EV charging infrastructure.
• This research provides a valuable framework for optimizing the planning of future EV charging networks.