Virtual bidding provides a mechanism for financial players to participate in wholesale day-ahead (DA) electricity markets. The price difference between DA and real-time (RT) markets creates financial arbitrage opportunities for financial players. Physical market participants (MP), referred to as participants with physical assets in this paper, can also take advantage of virtual bidding but in a different way, which is to further amplify the value of their physical assets. Therefore, this work proposes a model for such physical MPs to maximize the profits. This model employs a bi-level optimization approach, where the upper-level subproblem maximizes the total profit from both physical generations and virtual transactions while the lower-level model mimics the multi-period network-constrained DA market clearing process. In this model, uncertainties associated with other MPs as well as RT market prices are considered. Moreover, the conditional value-at-risk (CVaR) metric is utilized to measure the risk of diverse strategies. The optimal strategy of the strategic physical MP is derived by solving this bi-level optimization model. The proposed bi-level model is transformed to a single level mixed integer linear programming (MILP) model using Karush-Kuhn-Tucker (KKT) optimality conditions and the duality theory. Case studies show the effectiveness of the proposed method and reveal physical MPs may choose to deploy virtual transactions in a very different way than pure financial MPs.

中文翻译：

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日前电力市场中具有虚拟竞价能力的实体市场参与者的最佳竞价策略


虚拟竞价为金融机构参与批发日前（DA）电力市场提供了一种机制。 DA 和实时 (RT) 市场之间的价格差异为金融参与者创造了金融套利机会。实物市场参与者（MP），本文中指拥有实物资产的参与者，也可以利用虚拟竞价，但方式不同，即进一步放大其实物资产的价值。因此，这项工作提出了此类实体议员利润最大化的模型。该模型采用双层优化方法，其中上层子问题最大化物理发电和虚拟交易的总利润，而下层模型模仿多周期网络约束的 DA 市场清算过程。在该模型中，考虑了与其他 MP 以及 RT 市场价格相关的不确定性。此外，条件风险价值（CVaR）指标用于衡量不同策略的风险。通过求解该双层优化模型导出了战略物理MP的最优策略。使用 Karush-Kuhn-Tucker (KKT) 最优条件和对偶理论将所提出的双层模型转换为单层混合整数线性规划 (MILP) 模型。案例研究表明了所提出方法的有效性，并揭示了实体 MP 可能会选择以与纯金融 MP 截然不同的方式部署虚拟交易。