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arXiv · 2404.09460

Optimal Real-time Bidding Strategy For EV Aggregators in Wholesale Electricity Markets

Shihan Huang·Dongkun Han·John Zhen Fu Pang·Yue Chen
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Abstract

With the rapid growth of electric vehicles (EVs), EV aggregators have been playing a increasingly vital role in power systems by not merely providing charging management but also participating in wholesale electricity markets. This work studies the optimal real-time bidding strategy for an EV aggregator. Since the charging process of EVs is time-coupled, it is necessary for EV aggregators to consider future operational conditions (e.g., future EV arrivals) when deciding the current bidding strategy. However, accurately forecasting future operational conditions is challenging under the inherent uncertainties. Hence, there demands a real-time bidding strategy based solely on the up-to-date information, which is the main goal of this work. We start by developing an online optimal EV charging management algorithm for the EV aggregator via Lyapunov optimization. Based on this, an optimal real-time bidding strategy (bidding cost curve and bounds) for the aggregator is derived. Then, an efficient yet practical algorithm is proposed to obtain the bidding strategy. It shows that with the proposed bidding strategy, the aggregator's profit is nearly offline optimal. Moreover, the wholesale electricity market clearing result aligns with the individual aggregator's optimal charging strategy given the prices. Case studies against several benchmarks are conducted to evaluate the performance of the proposed method.

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Shihan Huang, Dongkun Han, John Zhen Fu Pang, Yue Chen. 2024-04-15. Optimal Real-time Bidding Strategy For EV Aggregators in Wholesale Electricity Markets. https://arxiv.org/abs/2404.09460

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