论文标题
学习主动限制以有效解决线性二聚体问题：应用于发电机战略招标问题
Learning Active Constraints to Efficiently Solve Linear Bilevel Problems: Application to the Generator Strategic Bidding Problem
论文作者
论文摘要
二重奏编程可用于在电力系统领域（例如战略招标）中提出许多问题。但是，对混合整体线性计划对二聚体问题的共同重新进行了重新制定，因此难以解决此类问题，这阻碍了他们在现实生活中的实施。在本文中，我们通过引入决策树来了解低级问题的主动限制，同时避免引入二进制组和大-M常数，从而显着提高了解决方案速度和障碍。机器学习的应用通过将主动约束的选择转移到离线过程来减少在线解决时间，并且当必须多次解决相同的问题时，变得尤其有益。我们将我们的方法应用于电力市场中发电机的战略招标，在这些发电机中，发电机多次解决相同的问题来解决不同的负载需求或可再生生产。开发了三种方法并将其应用于战略发电机的问题，较低级别的DCOPF。这些方法是启发式方法，因此不能提供最佳或解决方案质量的保证。但是，我们表明，对于不同尺寸的网络，计算负担大大减轻了，而我们还设法找到了以前棘手的战略招标问题的解决方案。
Bilevel programming can be used to formulate many problems in the field of power systems, such as strategic bidding. However, common reformulations of bilevel problems to mixed-integer linear programs make solving such problems hard, which impedes their implementation in real-life. In this paper, we significantly improve solution speed and tractability by introducing decision trees to learn the active constraints of the lower-level problem, while avoiding to introduce binaries and big-M constants. The application of machine learning reduces the online solving time, by moving the selection of active constraints to an offline process, and becomes particularly beneficial when the same problem has to be solved multiple times. We apply our approach to the strategic bidding of generators in electricity markets, where generators solve the same problem many times for varying load demand or renewable production. Three methods are developed and applied to the problem of a strategic generator, with a DCOPF in the lower-level. These methods are heuristic and as so, do not provide guarantees of optimality or solution quality. Yet, we show that for networks of varying sizes, the computational burden is significantly reduced, while we also manage to find solutions for strategic bidding problems that were previously intractable.
