ADMM-based decomposed DNN+RLT Relaxations for Completely Positive Models in Electricity Market Clearing

TL;DR

This paper introduces ADMM-based decomposed DNN+RLT relaxations for completely positive models to improve bounds and computational efficiency in large-scale electricity market clearing problems.

Contribution

It develops novel relaxations and decomposition techniques for CPP reformulations, enhancing bound quality and scalability in complex electricity market models.

Findings

DNN+RLT relaxations tighten LP bounds significantly.

Decomposition reduces computational effort in large-scale problems.

ADMM with adaptive penalty enables certified early termination.

Abstract

The day-ahead electricity market clearing with nonconvex order types can be formulated as a mixed-integer linear program (MILP), but its LP relaxation may provide weak bounds, and exact solutions can become computationally intractable in large-scale or extended market settings. We study a welfare-maximizing clearing model with elementary hourly orders, block orders with logical acceptance constraints, and flexible hourly orders. Starting from a compact MILP formulation, we derive an equivalent completely positive programming (CPP) reformulation via matrix lifting and propose relaxed CPP variants that further reduce the modeling burden while maintaining strong bounds. We then develop tractable doubly nonnegative (DNN) relaxations, including decomposed formulations that exploit the problem structure by using smaller positive semidefinite matrices. To further strengthen these bounds, we introduce reformulation-linearization technique (RLT) inequalities tailored to the decomposed structure. To tackle the challenge of large-scale DNNs, we design an alternating direction method of multipliers (ADMM) with adaptive penalty updates and rigorous dual lower bounds, enabling certified early termination. Computational experiments on synthetic instances show that the proposed DNN+RLT relaxations substantially tighten LP bounds, while decomposition and first-order methods significantly reduce computational effort.