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Propose multi-objective optimization method to generate Peledo optimal electric vehicle charging strategy
In this paper, researchers from the School of Electrical Engineering and Automation at Fuzhou University and the Department of Electrical Engineering at Yuanzhi University in Taiwan—Guan Yuliang, Wang Jinhua, and Qiu Weiyu—address the challenge of electric vehicle (EV) charging scheduling within a smart grid environment, specifically for taxis. Published in the 12th issue of *Electrical Technology* in 2017, the study explores how charging stations can manage EV charging rates to maximize their revenue while ensuring the power system maintains an optimal load factor for stability.
The dual objectives of maximizing revenue and load factor create a multi-objective optimization problem. To tackle this, the authors propose a multi-objective optimization method that generates a Pareto-optimal charging strategy. The effectiveness of the approach is validated through numerical analysis, demonstrating its practical application in real-world scenarios.
With the global energy crisis intensifying and concerns over fossil fuel depletion, air pollution, and climate change growing, governments and automotive industries are increasingly focused on developing sustainable transportation solutions. Electric vehicles have emerged as a promising solution, but their widespread adoption poses challenges for power grids due to the increased demand for electricity.
It is projected that by 2050, China could have as many as 200 million electric vehicles, requiring a total charging capacity of 330 million kilowatts. This surge in EVs will significantly impact power supply systems, making it crucial to develop efficient charging strategies and pricing models. As a result, the power market must evolve to support these changes, enabling better coordination between grid operators and EV users.
Charging stations play a vital role in managing grid load. By optimizing the charging rate of EVs, they can help maintain grid stability and reduce peak demand. This paper discusses the mathematical model of charging stations and their service objects, introduces the electricity price strategy under a smart grid, and presents a multi-objective optimization framework.
To simplify the analysis, the study divides the day into 48 time slots, each lasting 30 minutes. It outlines the scale of the charging station, which can handle up to 20 electric vehicles simultaneously, and includes both plug-in electric taxis and household EVs. The paper also examines different electricity pricing strategies, such as real-time pricing and time-of-use tariffs, to guide user behavior and balance grid load.
A key contribution of the research is the use of a multi-objective immune algorithm to solve the optimization problem. This algorithm mimics the human immune system, generating diverse and convergent solutions that reflect the trade-off between revenue and load factor. Through iterative updates, it identifies Pareto-optimal solutions that offer balanced performance across multiple objectives.
Simulation results show that the proposed method significantly improves both the charging station’s daily revenue and its load factor. When compared to a fixed charging rate of 10 kW/h, the optimized strategy reduced peak power consumption, smoothed out load distribution, and increased the load factor by 15.3%. Additionally, the daily revenue rose by 3.4%, demonstrating the effectiveness of the approach.
This study highlights the importance of integrating multi-objective optimization techniques in smart grid applications, especially as the number of EVs continues to grow. By balancing economic and operational goals, charging stations can contribute to a more stable and efficient power system.