The Regulatory and Political Challenges to V2G

Noel, Lance; Rubens, Gerardo Zarazua de; Kester, Johannes; Sovacool, Benjamin K.

doi:10.1007/978-3-030-04864-8_5

Cited by 4 publications

(1 citation statement)

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“…V2G systems involve complex interactions between electric vehicles and the grid, considering factors such as varying energy demands, charging station capacities, fluctuating energy prices, and environmental considerations [37]. In this context, agile optimization (AO) can address these challenges inherent in V2G optimization, providing flexibility, parallelization capabilities, and the rapid generation of solutions for dynamic environments.…”

Section: Agile Optimizationmentioning

confidence: 99%

Optimization Challenges in Vehicle-to-Grid (V2G) Systems and Artificial Intelligence Solving Methods

Escoto,

Guerrero,

Ghorbani

et al. 2024

Applied Sciences

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Vehicle-to-grid (V2G) systems play a key role in the integration of electric vehicles (EVs) into smart grids by enabling bidirectional energy flows between EVs and the grid. Optimizing V2G operations poses significant challenges due to the dynamic nature of energy demand, grid constraints, and user preferences. This paper addresses the optimization challenges in V2G systems and explores the use of artificial intelligence (AI) methods to tackle these challenges. The paper provides a comprehensive analysis of existing work on optimization in V2G systems and identifies gaps where AI-driven algorithms, machine learning, metaheuristic extensions, and agile optimization concepts can be applied. Case studies and examples demonstrate the efficacy of AI-driven algorithms in optimizing V2G operations, leading to improved grid stability, cost optimization, and user satisfaction. Furthermore, agile optimization concepts are introduced to enhance flexibility and responsiveness in V2G optimization. The paper concludes with a discussion on the challenges and future directions for integrating AI-driven methods into V2G systems, highlighting the potential for these intelligent algorithms and methods.

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Section: Agile Optimizationmentioning

confidence: 99%