EVLearn: Extending the CityLearn Framework with Electric Vehicle Simulation
arxiv(2024)
摘要
Intelligent energy management strategies, such as Vehicle-to-Grid (V2G) and
Grid-to-Vehicle (G2V) emerge as a potential solution to the Electric Vehicles'
(EVs) integration into the energy grid. These strategies promise enhanced grid
resilience and economic benefits for both vehicle owners and grid operators.
Despite the announced prospective, the adoption of these strategies is still
hindered by an array of operational problems. Key among these is the lack of a
simulation platform that allows to validate and refine V2G and G2V strategies.
Including the development, training, and testing in the context of Energy
Communities (ECs) incorporating multiple flexible energy assets. Addressing
this gap, first we introduce the EVLearn, a simulation module for researching
in both V2G and G2V energy management strategies, that models EVs, their
charging infrastructure and associated energy flexibility dynamics; second,
this paper integrates EVLearn with the existing CityLearn framework, providing
V2G and G2V simulation capabilities into the study of broader energy management
strategies. Results validated EVLearn and its integration into CityLearn, where
the impact of these strategies is highlighted through a comparative simulation
scenario.
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