论文标题
一般的随机优化框架，用于收敛招标
A General Stochastic Optimization Framework for Convergence Bidding
论文作者
论文摘要
融合（虚拟）竞标是两组电力市场的重要组成部分，因为它可以有效地减少日常和实时市场之间的差异。因此，对虚拟参与者的竞标策略进行了广泛的研究，旨在获得最佳的出价来提交日间市场。在本文中，我们介绍了一个基于价格的一般随机优化框架，以获得最佳的收敛竞标曲线。在此框架内，我们开发了一种基于计算的基于线性编程的优化模型，该模型同时产生了出价和数量。我们还表明，通用模型中的不同近似值和简化自然导致了最新的融合竞标方法，例如自我安排和机会主义方法。我们的一般框架还提供了一种简单的方法来比较这些模型的性能，这是通过加利福尼亚（CAISO）市场的数值实验证明的。
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.
