发表机构
N-SIDE; National Technical University of Athens（N-SIDE; 雅典国立技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对跨区域平衡容量市场的备用输送能力问题，提出两种可扩展内近似方法及列生成算法，其扩展性优于随机规划，可适配泛欧洲市场及能源与平衡容量协同优化。
AI 中文摘要
在输电网物理特性被精准纳入市场出清模型的电力市场中，某些跨区域电力交易仅能在其他交易同时发生时进行，这给实时备用激活仍存在不确定性的跨区域平衡容量市场带来了挑战。确保所有激活场景下的备用输送能力可自然建模为随机规划（SP）问题，该问题的规模随网络中节点数量呈指数增长，在实际应用中扩展性极差。我们首先证明，激活场景可通过“订单簿无关方式”表达，将问题转化为网络建模问题，已能提升计算性能。随后，我们引入通用内近似原理，基于该原理推导了两种可扩展的内近似方法及一种列生成算法，用于求解其中一种近似问题。第一种内近似方法为从业者熟知，与描述多面体中箱体（或类似地，描述基于潮流的域内可用传输容量（ATC）域的并集）的基本结果相关；第二种模型、其关联的列生成算法及有限维重构基于半无限线性规划和鲁棒线性优化。我们将这些内近似方法与精确SP公式进行对比，为便于比较，我们也通过Danzig-Wolfe分解求解了精确SP公式。数值结果表明，这些内近似方法的扩展性远优于随机规划公式，同时能获得跨区域交易的大部分收益。这些方法对未来泛欧洲跨区域平衡容量市场具有特殊意义，还可适配能源与平衡容量产品的协同优化。
英文摘要
In power markets where the physics of the transmission grid is closely represented in market clearing models, certain cross-zonal power exchanges can take place only if other exchanges occur concurrently. This leads to challenges in cross-zonal balancing capacity markets, where the activation of reserves in real time remains uncertain. Ensuring reserve deliverability in all activation scenarios is naturally modeled as a stochastic programming (SP) problem, whose size grows exponentially with the number of locations in the network. This formulation scales poorly in real-world applications. We first show that activation scenarios can be expressed in an order-book-agnostic way, reducing the challenge to a network modeling problem and improving computational performance. We then introduce a general inner approximation principle that we use to derive two scalable inner approximations and one column generation algorithm for tackling one of the approximations. The first inner approximation is well known to practitioners, and relates to a basic result for describing boxes in a polytope, while the second model, its associated column generation algorithm and finite-dimensional reformulation are based on semi-infinite linear programming and robust linear optimization. We compare the inner approximations to the exact SP formulation, which we also solve via a Dantzig-Wolfe decomposition for comparison purposes. Numerical results show that these inner approximations are much more scalable than the stochastic programming formulation, while reaping most of the benefits of cross-zonal exchanges. The approaches are of particular interest for future pan-European cross-zonal balancing capacity markets, and can also accommodate co-optimization of energy and balancing capacity products.
