publications

<div class=”publications”;>

2024

1. Aggregation of Bilinear Bipartite Equality Constraints and its Application to Structural Model Updating Problem

   Santanu S Dey^*, Dahye Han^*, and Yang Wang^*

   arXiv preprint arXiv:2410.14163, 2024

   Abs arXiv

   In this paper, we study the strength of convex relaxations obtained by convexification of aggregation of constraints for a set S described by two bilinear bipartite equalities. Aggregation is the process of rescaling the original constraints by scalar weights and adding the scaled con- straints together. It is natural to study the aggregation technique as it yields a single bilinear bipartite equality whose convex hull is already understood from previous literature. On the the- oretical side, we present sufficient conditions when conv(S) can be described by the intersection of convex hulls of a finite number of aggregations, examples when conv(S) can only be obtained as the intersection of the convex hull of an infinite number of aggregations, and examples when conv(S) cannot be achieved exactly from the process of aggregation. Computationally, we ex- plore different methods to derive aggregation weights in order to obtain tight convex relaxations. We show that even if an exact convex hull may not be achieved using aggregations, including the convex hull of an aggregation often significantly tightens the outer approximation of conv(S). Finally, we apply the aggregation method to obtain convex relaxation for the structural model updating problem and show that this yields better bounds within a branch-and-bound tree as compared to not using aggregations.

2. Regularized MIP Model for Optimal Power Flow with Energy Storage Systems and its Applications

   Dahye Han, Nan Jiang, Santanu S Dey, and Weijun Xie

   arXiv preprint arXiv:2402.04406, 2024

   Abs arXiv

   Incorporating energy storage systems (ESS) into power systems has been studied in many recent works, where binary variables are often introduced to model the complementary nature of battery charging and discharging. A conventional approach for these ESS optimization problems is to relax binary variables and convert the problem into a linear program. However, such linear programming relaxation models can yield unrealistic fractional solutions, such as simultaneous charging and discharging. In this paper, we develop a regularized Mixed-Integer Programming (MIP) model for the ESS optimal power flow (OPF) problem. We prove that under mild condi- tions, the proposed regularized model admits a zero integrality gap with its linear programming relaxation; hence, it can be solved efficiently. By studying the properties of the regularized MIP model, we show that its optimal solution is also near-optimal to the original ESS OPF problem, thereby providing a valid and tight upper bound for the ESS OPF problem. The use of the regularized MIP model allows us to solve two intractable problems: a two-stage stochastic ESS OPF problem and a trilevel min-max-min network contingency problem.

2023

1. Confidence-aware graph neural networks for learning reliability assessment commitments

   Seonho Park, Wenbo Chen, Dahye Han, Mathieu Tanneau, and Pascal Van Hentenryck

   IEEE Transactions on Power Systems, 2023

   Abs HTML

   Reliability Assessment Commitment (RAC) Opti- mization is increasingly important in grid operations due to larger shares of renewable generations in the generation mix and in- creased prediction errors. Independent System Operators (ISOs) also aim at using finer time granularities, longer time horizons, and possibly stochastic formulations for additional economic and reliability benefits. The goal of this article is to address the compu- tational challenges arising in extending the scope of RAC formula- tions. It presents RACLEARN that 1) uses a Graph Neural Network (GNN) based architecture to predict generator commitments and active line constraints, 2) associates a confidence value to each commitment prediction, 3) selects a subset of the high-confidence predictions, which are 4) repaired for feasibility, and 5) seeds a state-of-the-art optimization algorithm with feasible predictions and active constraints. Experimental results on exact RAC formu- lations used by the Midcontinent Independent System Operator (MISO) and an actual transmission network (8965 transmission lines, 6708 buses, 1890 generators, and 6262 load units) show that the RACLEARN framework can speed up RAC optimization by factors ranging from 2 to 4 with negligible loss in solution quality.

2022

1. Risk-aware control and optimization for high-renewable power grids

   Neil Barry^*, Minas Chatzos^*, Wenbo Chen^*, Dahye Han^*, Chaofan Huang^*, Roshan Joseph^*, Michael Klamkin^*, Seonho Park^*, Mathieu Tanneau^*, Pascal Van Hentenryck^*, and  others

   arXiv preprint arXiv:2204.00950, 2022

   Abs arXiv

   The transition of the electrical power grid from fossil fuels to renewable sources of energy raises fundamental challenges to the market-clearing algorithms that drive its operations. Indeed, the increased stochasticity in load and the volatility of renewable energy sources have led to significant increases in prediction errors, affecting the reliability and efficiency of existing deterministic optimization models. The RAMC project was initiated to investigate how to move from this deterministic setting into a risk-aware framework where uncertainty is quantified explicitly and incorporated in the market-clearing optimizations. Risk-aware market-clearing raises challenges on its own, primarily from a computational standpoint. This paper reviews how RAMC approaches risk-aware market clearing and presents some of its innovations in uncertainty quantification, optimization, and machine learning. Experimental results on real networks are presented.

</div>