SearcharxivSearch

arXiv · 2004.07077

High Dimensional Three-Periods Locally Ideal MIP Formulations for the UC Problem

Abstract

The thermal unit commitment (UC) problem often can be formulated as a mixed integer quadratic programming (MIQP), which is difficult to solve efficiently, especially for large-scale instances. The tighter characteristic reduces the search space, therefore, as a natural conse-quence, significantly reduces the computational burden. In the literature, many tightened formulations for single units with parts of constraints were reported without presenting how they were derived. In this paper, a sys-tematic approach is developed to formulate the tight formulations. The idea is using more new variables in high dimension space to capture all the states for single units within three periods, and then, using these state variables systematic derive three-periods locally ideal expressions for a subset of the constraints in UC. Meanwhile, the linear dependence relations of those new state variables are leveraged to keep the compactness of the obtained formulations. Based on this approach, we propose two tighter models, namely 3P-HD and 3P-HD-Pr. The proposed models and other four state-of-the-art models were tested on 51 instances, including 42 realistic instances and 9 8-unit-based instances, over a scheduling period of 24 h for systems ranging from 10 to 1080 generating units. The simulation results show that our proposed MIQP UC formulations are the tightest and can be solved most efficiently. After using piecewise technique to approxi-mate the quadratic operational cost function, the six UC MIQP formulations can be approximated by six corre-sponding mixed-integer linear programming (MILP) formulations. Our experiments show that the proposed 3P-HD and 3P-HD-Pr MILP formulations also perform the best in terms of tightness and solution times.

Explore related subjects

Keep this discovery

BibTeXRIS

Linfeng Yang, Wei Li, Yan Xu, Cuo Zhang, Beihua Fang. 2020-04-15. High Dimensional Three-Periods Locally Ideal MIP Formulations for the UC Problem. https://arxiv.org/abs/2004.07077

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Deterministic and Random Bipartite Matching on General Networks: Convex Flow Reformulation, Asymptotic Properties, and Fast Algorithms

Minimum-distance bipartite matching on general networks has numerous applications various fields. This paper first focuses on deterministic problems and presents an exact edgewise-separable convex-flow reformulation. By introducing a smooth monotone rearrangement approximation of the edge-wise imbalance profiles, the convex-flow reformulation's can be solved efficiently. If we further conduct a first-order resistance-based approximation of the convex program, a one-step Laplacian-based estimator can be analytically derived in closed forms. The paper also studies random problems where supply and demand points are randomly distributed. We show that the expected optimal matching distance scales with the square root of the number of points if the supply/demand point distributions are identical, or linearly otherwise. In the former case, the optimal flow is proven to be centered, symmetric, and sub-Gaussian. In the latter case, the limiting resistance network characterizes how supply-demand imbalance is redistributed and motivates a fast algorithm that approximate the optimal flow based on the limiting resistance. Numerical experiments show that the proposed estimators closely approximate the exact matching cost while substantially reducing computation time. The proven theoretical properties of the random matching solution are numerically verified by large-scale Monte Carlo simulations.

math.OC

Conformal-DRO: Distributionally Robust Optimization with Conformalized Ambiguity Set

Data-driven distributionally robust optimization (DRO) typically treats the conditional outcome law as fixed and uses ambiguity sets to capture estimation error. This paper studies latent distributional heterogeneity, where each instance has an unobserved law but contributes only one observation, so uncertainty persists even if the mixture law is known. We propose Conformal-DRO, which uses nested conformal regions to construct an ambiguity set for the future latent law. Under exchangeability, the set covers this law with probability at least $1-\alpha$ in finite samples, without estimating underlying latent laws or their mixing mechanism. The conformal path induces a data-driven transport geometry, while $\alpha$ determines the radius. The worst-case problem reduces to a finite linear program over conformal shells and admits sparse adversarial solutions. The resulting robust value provides a finite-sample certificate for the selected decision's expected cost.

math.OC

The best approximation tuple: an extension of the Cheney-Goldstein algorithm and results to the multiple sets case

In this paper we extend the algorithm and several results published in the celebrated 1959 paper of Cheney and Goldstein about the best approximation pair (BAP) problem in two separate directions. One is the consideration of more than two sets. The other is the ability to handle each set as an intersections of a finite family of sets. We call the resulting problem the "Best Approximation Tuple (BAT) problem". The fundamental observation that leads to this generalizations is to recognize and handle one set (the "pivot set") as different from the remaining sets (the "satellite sets") instead of seeking cycles as the minimizers of a target functional. This enable us to overcome a certain theoretical obstacle related to cycles and minimizers of general functionals. We prove the convergence of the algorithm to the unique solution of the problem in the Euclidean case with strictly convex and compact satellite sets. Because of the lack of Fej\'er monotonicity, our convergence analysis is not standard, and is based on almost unknown properties of orthogonal projections regarding equality and inequality in the definition of nonexpansiveness.

math.OC