Models and Algorithms for Reserve Deliverability in Cross-Zonal Balancing Capacity Markets

Abstract

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 already 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 (or similarly, describing the union of ATC domains within a flow-based domain), 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 Danzig-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.