Some Optimization Strategies for Smart Charging of Electric Vehicles
Keywords: Smart Charging, Genetic algorithm, PSO
Abstract: Background: The widespread adoption of electric vehicles (EVs) is critical for mitigating climate change and transitioning towards a more sustainable future. As the number of EVs on the roads increases, the demand for efficient smart charging solutions becomes more pressing. However, optimizing smart charging for EVs is a complex area that has not yet been fully explored. This study intends to tackle this pressing challenge by lever- aging advanced computational techniques, specifically genetic algorithms (GA) and particle swarm optimization (PSO), to enhance the scheduling processes in smart charging hubs.
Approach: In our research, we thoroughly investigate five existing objective functions that are commonly used in the context of smart charging. In addition to these established methods, we introduce an innovative safety-aware loss function designed to ensure the reliability and safety of the charging process. This leads us to develop a comprehensive framework consisting of a total of twelve distinct optimization strategies, each tailored to optimize different aspects of smart charging operations.
Result: To evaluate the effectiveness and computational efficiency of these strategies, we conduct a series of rigorous numerical experiments. These experiments not only assess the performance of each strategy under varying conditions but also provide insights into their strengths and limitations. Furthermore, we delve into the scalability of the proposed optimization framework, exploring how well it adapts to larger networks of charging hubs and an increasing number of users.
Conclusion: Based on our findings, we offer practical recommendations aimed at facilitating the implementa- tion of smart charging hubs in real-world scenarios. These insights are designed to assist policymakers, urban planners, and technology developers in crafting strategies that support the sustainable integration of EVs into our transportation systems while addressing the complexities of energy management and user safety.
Submission Number: 632
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