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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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Minimizing System Entropy: A Dual-Phase Optimization Approach for EV Charging Scheduling.

Wenpeng Yuan¹, Lin Guan¹

¹School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China.

Entropy (Basel, Switzerland)

|March 28, 2025

Summary

This study introduces a two-phase optimization for electric vehicle (EV) charging in rural grids, combining particle swarm optimization and Q-learning. The method enhances grid stability and user satisfaction by minimizing voltage deviations and overloads.

Keywords:

Q-learning distribution networks electric vehicle charging minimizing system entropy particle swarm optimization rural power systems

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Area of Science:

Electrical Engineering
Optimization Theory
Artificial Intelligence

Background:

Rural distribution networks face challenges managing variable electric vehicle (EV) charging loads.
Uncertainty and disorder in power distribution increase due to uncoordinated EV charging.

Purpose of the Study:

To develop a robust two-phase optimization strategy for EV charging scheduling in rural networks.
To minimize voltage deviations, line overloads, and system entropy while maximizing user satisfaction.

Main Methods:

A hybrid approach combining Particle Swarm Optimization (PSO) for initial planning and Q-learning for real-time adaptation.
PSO optimizes charging schedules to reduce voltage deviations and overloads.
Q-learning dynamically adjusts the charging plan based on real-time grid conditions.

Main Results:

Voltage deviation reduced from 5.8% to 1.9%.
Maximum load factor decreased from 95% to 82%.
Average customer satisfaction improved from 75% to 88%.

Conclusions:

The proposed two-phase strategy effectively balances grid reliability and user convenience for EV integration.
This method offers a practical solution for managing EV charging in rural power systems.
The strategy significantly reduces system entropy and enhances overall grid performance.