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This paper introduces a novel scheduling framework designed to manage the charging of electric vehicles (EVs) in a way that considers its effects on the power grid. Leveraging the Alternating Direction Method of Multipliers (ADMM), the methodology offers a significant advantage by enabling decentralized sub-problems, allowing for efficient and rapid solutions. The methodology developed as an algorithmic framework incorporates various scheduling approaches for EV charging, including demand management techniques like valley filling and peak shaving, along with real-time pricing (RTP) considerations. These strategies aim to modify individual electricity consumption patterns to reduce peak demand, ultimately enhancing energy efficiency and ensuring the stability of the power system. The results of the study highlight the crucial role of distributed optimization in improving both demand management strategies and cost objectives. The results indicated that the proposed method shows significant improvement in overall energy efficiency when compared to the state-of-the-art centralized convex optimization framework.
Aygün et al. (Tue,) studied this question.
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