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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 electric vehicle (EV) charging scheduling problem for taxis within a smart grid environment. Published in the 12th issue of *Electrical Technology* in 2017, the study explores how charging stations can manage charging rates to maximize revenue while ensuring system stability by optimizing load factors.
The dual objective of maximizing charging station revenue and improving the power system's load factor leads to a multi-objective optimization problem. To address this, the authors propose a multi-objective optimization method that generates a Pareto-optimal charging strategy for EVs. The effectiveness of the proposed approach is validated through numerical analysis, demonstrating its practical value in real-world applications.
With the growing global energy crisis, declining fossil fuel reserves, rising air pollution, and increasing temperatures, governments and automotive industries are increasingly focused on energy efficiency and emission reduction as key directions for future vehicle technologies. Electric vehicles offer a promising solution to these challenges. However, the large-scale adoption of EVs poses significant challenges to the power grid, as it increases overall electricity demand and complicates grid management.
By 2050, it is estimated that China will have 200 million electric vehicles, requiring a total charging capacity of 330 million kilowatts. This surge in EVs will significantly impact the power supply infrastructure. Different electricity pricing models influence when and how EVs charge, making the interaction between the grid and EVs more complex. A more advanced power market is needed to support this evolving landscape.
To maintain grid stability, charging stations must optimize their charging schedules and rates. As operators of the grid, they can adjust the charging rate of each EV to balance the load across different time slots. This helps prevent overloads and ensures smooth power distribution.
This paper is structured into several sections. First, it discusses the mathematical model of the charging station and its service objects. Then, it introduces the electricity price strategy under smart grid conditions. Finally, it presents the multi-objective optimization problem and proposes a solution. For clarity, the day is divided into 48 time slots, each lasting 30 minutes.
The charging station plays a crucial role in the future, much like gas stations today. It can regulate the charging rate of household EVs, ranging from 5 kW/h to 15 kW/h. The simulated station has 20 charging piles, capable of handling up to 20 EVs simultaneously. These include Plug-in Electric Taxis (PETs) and private EVs.
Under the smart grid, electricity pricing strategies such as real-time pricing, day-ahead pricing, and time-of-use tariffs influence consumer behavior. This paper uses actual electricity prices from Maryland Interconnected Power Company (PJM) in Pennsylvania, which vary between peak and off-peak hours. These prices are used to simulate daily charging patterns across 48 time slots.
The system process involves collecting data on the number of EVs, battery levels, and charging needs. This information is sent to a cloud-based dispatch center, which calculates the optimal charging rates for each time slot. The results are then fed back to the charging station, enabling efficient charging operations.
The multi-objective optimization problem focuses on balancing two conflicting goals: maximizing revenue and improving the load factor. A Pareto-optimal solution is sought, where improvements in one goal may lead to trade-offs in another. To solve this, the authors employ a multi-objective immune algorithm, inspired by the human immune system's ability to detect and respond to threats.
This algorithm enhances convergence and diversity, making it effective for solving complex optimization problems. It mimics the immune system’s memory function, allowing faster adaptation to similar scenarios. In this context, the objective functions are treated as antigens, and solutions are considered antibodies, with fitness reflecting their suitability.
Simulation results show 30 Pareto-optimal solutions for a single day of charging. Due to the random nature of battery levels upon arrival, each simulation yields slightly different outcomes. A multi-criteria decision-making approach using Manhattan Distance is applied to select the best solution based on the ideal vector.
When comparing fixed versus optimized charging rates, the results reveal significant improvements. The load factor increased from 0.413 to 0.476, a 15.3% improvement, while daily revenue rose from $1,208 to $1,249, a 3.4% increase. These findings demonstrate the effectiveness of the proposed method in enhancing both grid stability and charging station profitability.
In conclusion, this study highlights the importance of integrating multiple objectives—such as revenue and load factor—into EV charging strategies. By leveraging advanced algorithms, the research provides a practical framework for managing EV charging in a smart grid, supporting sustainable and efficient energy use.