EURO 2025 Leeds
Abstract Submission

1633. Exploring Strategies for Refining Column-and-Constraint Generation in Solving Two-Stage P2P Energy Trading Models under Uncertainty

Invited abstract in session TA-27: Applications of Optimization under Uncertainty, stream Stochastic and Robust optimization.

Tuesday, 8:30-10:00
Room: Maurice Keyworth G.02

Authors (first author is the speaker)

1. Albert Farriol Salas
IREC
2. Fernando García
USACH
3. Josh Eichman
Energy Systems Analytics Research Group, Institut de Recerca en Energia de Catalunya

Abstract

This study presents two strategies for applying the column-and-constraint generation (CCG) algorithm to large-scale optimization problems. These strategies are designed to address potential memory limitations by delaying the expansion of the master model and incorporating initial scenarios to streamline the problem-solving process. The proposed methods are designed to improve the performance of the algorithm in broader large-scale optimization problems although they are tested in a peer-to-peer energy trading model. To evaluate the effectiveness of these strategies, simulations are conducted using the IEEE 33-bus system. The analysis focuses on the impact of the proposed strategies on execution time, the number of iterations, and the handling of unused scenarios in the master model. Initial tests are performed with all strategies and their combinations, followed by an increase in the number of total scenarios to further examine the performance of the best combinations in managing larger-scale optimization problems. The results indicate that selection of the most appropriate strategy is a tradeoff between the size of the problem and the execution time. Strategies were proposed that can reduce execution time by up to 29% compared to the classic CCG formulation, and other strategies were able to reduce the size of the problem by more than 20%, enabling solution of larger problems, at the expense of a moderate increase in execution time.

Keywords

Status: accepted

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