Ádám Sleisz | Searcharxiv

arXiv2025

Overprocurement of balancing capacity may increase the welfare in the cross-zonal energy-reserve coallocation problem

When the traded energy and reserve products between zones are co-allocated to optimize the infrastructure usage, both deterministic and stochastic flows have to be accounted for on interconnector lines. We focus on allocation models, which guarantee deliverability in the context of the portfolio bidding European day-ahead market framework, assuming a flow-based description of network constraints. In such models, as each unit of allocated reserve supply implies additional cost, it is straightforward to assume that the amount of allocated reserve is equal to the accepted reserve demand quantity. However, as it is illustrated by the proposed work, overprocurement of reserves may imply counterintuitive benefits. Reserve supplies not used for balancing may be used for congestion management, thus allowing valuable additional flows in the network.

q-fin.GN↗

arXiv2024

Bid-aggregation based clearing of day-ahead electricity markets

In this work we propose a heuristic clearing method of day-ahead electricity markets. In the first part of the process, a computationally less demanding problem is solved using an approximation of the cumulative demand and supply curves, which are derived via the aggregation of simple bids. Based on the outcome of this problem, estimated ranges for the clearing prices of individual periods are determined. In the final step, the clearing for the original bid set is solved, taking into account the price ranges determined previously as constraints. Adding such constraints reduces the feasibility region of the clearing problem. By removing simple bids whose acceptance or rejection is already determined by the assumed price range constraints, the size of the problem is also significantly reduced. Via simple examples, we show that due to the possible paradox rejection of block bids the proposed bid-aggregation based approach may result in a suboptimal solution or in an infeasible problem, but we also point out that these pitfalls of the algorithm may be avoided by using different aggregation patterns. We propose to construct multiple different aggregation patterns and to use parallel computing to enhance the performance of the algorithm. We test the proposed approach on setups of various problem sizes, and conclude that in the case of parallel computing with 4 threads a high success rate and a significant gain in computational speed may be achieved.

cs.GT↗