The existing bidding models for retailers managing electric vehicles (EVs) in distribution-level day-ahead (DA) electricity markets have not fully addressed EVs’ temporal distribution, charging and discharging management, or bidding curves. To address these challenges, this paper proposes a DA optimal bidding model for a retailer with flexible participation of EVs. First, the investigating period for DA optimal bidding is modeled to cover the connecting periods of all EVs that may potentially impact the DA power demand prediction. Then, incentive mechanisms for charging and discharging are proposed to enable retailers to manage EVs under uncertain market prices and EV parameters while also considering the battery degradation cost and the preferences of EV users based on a social survey. The optimal charging model based on incentive mechanisms for charging-discharging helps EVs minimize their energy purchase costs under different price scenarios. Based on the minimum EV energy purchase cost, the optimal bidding model of a retailer aims to achieve the maximum bidding profit considering the conditional value at risk (CVaR), with the step bidding curves and the temporal differences for different time intervals considered. Simulations validate the proposed model under flexible incentive mechanism for charging and incentive mechanism for discharging participation of EVs.

中文翻译：

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灵活参与电动汽车的零售商日前最优投标

在配电级日前 (DA) 电力市场中管理电动汽车 (EV) 的零售商的现有投标模型尚未完全解决电动汽车的时间分布、充电和放电管理或投标曲线。为了应对这些挑战，本文提出了一种电动汽车灵活参与的零售商 DA 最优投标模型。首先，对 DA 最优投标的调查期进行建模，以涵盖可能影响 DA 电力需求预测的所有 EV 的连接期。然后，基于社会调查，提出了充电和放电激励机制，使零售商能够在市场价格和电动汽车参数不确定的情况下管理电动汽车，同时考虑电池退化成本和电动汽车用户的偏好。基于充放电激励机制的最优充电模型有助于电动汽车在不同价格场景下最大限度地降低能源购买成本。基于最小的电动汽车能源购买成本，零售商的最优投标模型以在考虑条件风险价值（CVaR）的情况下实现最大的投标利润为目标，考虑了阶跃投标曲线和不同时间间隔的时间差异。仿真在灵活的充电激励机制和电动汽车放电参与激励机制下验证了所提出的模型。零售商的最优投标模型旨在考虑条件风险价值（CVaR）实现最大投标利润，其中考虑了阶跃投标曲线和不同时间间隔的时间差异。仿真在灵活的充电激励机制和电动汽车放电参与激励机制下验证了所提出的模型。零售商的最优投标模型旨在考虑条件风险价值（CVaR）实现最大投标利润，其中考虑了阶跃投标曲线和不同时间间隔的时间差异。仿真在灵活的充电激励机制和电动汽车放电参与激励机制下验证了所提出的模型。