Convergence (virtual) bidding is an important part of two-settlement electric power markets as it can effectively reduce discrepancies between the day-ahead and real-time markets. Consequently, there is extensive research into the bidding strategies of virtual participants aiming to obtain optimal bids to submit to the day-ahead market. In this paper, we introduce a price-based general stochastic optimization framework to obtain optimal convergence bid curves. Within this framework, we develop a computationally tractable linear programming-based optimization model, which produces bid prices and volumes simultaneously. We also show that different approximations and simplifications in the general model lead naturally to state-of-the-art convergence bidding approaches, such as self-scheduling and opportunistic approaches. Our general framework also provides a straightforward way to compare the performance of these models, which is demonstrated by numerical experiments on the California (CAISO) market.

翻译：差价（虚拟）竞价是双重结算电力市场的重要组成部分，因其能有效减小日前市场与实时市场间的偏差。因此，针对虚拟参与者旨在获取最优日前市场竞标策略的研究已广泛展开。本文提出了一种基于价格信号的通用随机优化框架，用于获取最优差价竞价曲线。在该框架内，我们构建了一个计算可行的线性规划优化模型，可同步生成竞标价格与电量。研究同时表明，通用模型中的不同近似与简化处理自然衍生出当前最先进的差价竞价方法，如自调度型与机会型策略。该通用框架还提供了一种直接比较各模型性能的途径，加州独立系统运营商（CAISO）市场的数值实验验证了其有效性。