联合优化提供实时频率调节服务的电动汽车聚合商的投标与功率分配
Co-optimizing Bidding and Power Allocation of an EV Aggregator Providing Real-time Frequency Regulation Service
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中文总结 AI 辅助
针对电动汽车聚合商投标与功率分配耦合问题，提出联合优化框架，结合随机规划与在线功率分配模型，提升其利润并降低衰减成本。
中文摘要 AI 辅助
电动汽车（EV）车队规模的快速扩张以及电池成本的持续下降，使得车到电网（V2G）服务成为现实。本文研究电动汽车聚合商（EVA）在调节市场中的投标问题与功率分配问题（即确定调节部署中各电动汽车的充放电功率）之间的相互作用。尽管这两个问题相互耦合，但出于复杂性考虑，它们通常被视为解耦问题并分别进行优化。然而，未考虑投标与功率分配的耦合会导致电动汽车聚合商（EVA）的利润下降。本文提出一种用于联合优化EVA在调节市场中的投标与功率分配的框架。投标模型被构建为随机规划问题，在离散化的调节信号场景中嵌入功率分配。为满足调节部署的求解时间要求，本文进一步提出一种可在线求解的功率分配模型，该模型利用投标问题的拉格朗日乘子，确保分配结果与投标问题的最优解相对应。案例研究验证了所提框架在提高EVA利润、降低衰减成本方面的效果。
英文摘要
The rapidly expanding scale of electric vehicle (EV) fleets and continuously decreasing battery costs are making vehicle-to-grid services a reality. In this paper, we study the interaction between the problems of an EV aggregator's bidding in the regulation market and power allocation (i.e., determining the (dis)charging powers of the EVs in regulation deployment). Although the two problems are coupled, they are often regarded as decoupled and optimized separately for complexity issues. However, failing to consider the coupling of bidding and power allocation can lead to a decline in the profit of the EV aggregator (EVA). In this paper, we propose a framework for co-optimizing EVA bidding and power allocation in the regulation market. The bidding model is formulated as a stochastic programming problem with embedded power allocation in discretized regulation signal scenarios. To meet the solution time requirement for regulation deployment, we further propose a power allocation model that can be solved online. It utilizes the Lagrange multipliers from the bidding problem to ensure that the allocation results correspond to the optimal solution of the bidding problem. The effect of the proposed framework on improving EVA profits and reducing degradation costs is verified in the case study.
