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Chen, Richard Li-Yang

Publications and source records attributed to Chen, Richard Li-Yang.

18 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Robust Network Interdiction Considering Multiple Defense Resources

We extend the network interdiction problem in order to consider multiple options for defending arcs in a network. We apply our model to select an optimal set of countermeasures for nuclear material smuggling. In order account for the joint impact of multiple countermeasures, we consider multiple components of defense: situational awareness, detection, and interdiction. We develop a bi-level programming model, where the adversary's goal is to maximize damage to the system while the defender seeks to minimize damage by selecting countermeasure options under a restricted bud- get. Furthermore, since effectiveness of countermeasures is often difficult to assess with high accuracy, we use robust optimization to allow for parametric uncertainty. We test our method on a synthetic example of the U.S. supply chain network that includes rail, air, and maritime transportation between both domestic and international locations. For this test example, we find that increasing the budget for countermeasures has the greatest impact within the first 1-2% of the cost to implement all countermeasures and that the uncertainty tolerance has a greater impact on expected damage than the countermeasure budget does. Thus, when strategically selecting countermeasures it is important to precisely understand the effectiveness of each option and the adversary's probability of subverting them.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Chance-constrained economic dispatch with renewable energy and storage

Increased penetration of renewables, along with uncertainties associated with them, have transformed how power systems are operated. High levels of uncertainty means that it is not longer possible to guarantee operational feasibility with certainty, instead constraints are required to be satisfied with high probability. We present a chance-constrained economic dispatch model that efficiently integrates energy storage and high renewable penetration to satisfy renewable portfolio requirements. Specifically, it is required that wind energy contributes at least a prespecified ratio of the total demand and that the scheduled wind energy is dispatchable with high probability. We develop an approximated partial sample average approximation (PSAA) framework to enable efficient solution of large-scale chanceconstrained economic dispatch problems. Computational experiments on the IEEE-24 bus system show that the proposed PSAA approach is more accurate, closer to the prescribed tolerance, and about 100 times faster than sample average approximation. Improved efficiency of our PSAA approach enables solution of WECC-240 system in minutes.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Transmission expansion with smart switching under demand uncertainty and line failures

One of the major challenges in deciding where to build new transmission lines is that there is uncertainty regarding future loads, renewal generation output and equipment failures. We propose a robust optimization model whose transmission expansion solutions ensure that demand can be met over a wide range of conditions. Specifically, we require feasible operation for all loads and renewable generation levels within given ranges, and for all single transmission line failures. Furthermore, we consider transmission switching as an allowable recovery action. This relatively inexpensive method of redirecting power flows improves resiliency, but introduces computational challenges. Lastly, we present a novel algorithm to solve this model. Computational results are discussed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Algorithm to solve a chance-constrained network capacity design problem with stochastic demands and finite support

Here, we consider the problem of determining the capacity to assign to each arc in a given network, subject to uncertainty in the supply and/or demand of each node. This design problem underlies many real-world applications, such as the design of power transmission and telecommunications networks. We first consider the case where a set of supply/demand scenarios are provided, and we must determine the minimum-cost set of arc capacities such that a feasible flow exists for each scenario. We briefly review existing theoretical approaches to solving this problem and explore implementation strategies to reduce run times. With this as a foundation, our primary focus is on a chance-constrained version of the problem in which α% of the scenarios must be feasible under the chosen capacity, where α is a user-defined parameter and the specific scenarios to be satisfied are not predetermined. We describe an algorithm which utilizes a separation routine for identifying violated cut-sets which can solve the problem to optimality, and we present computational results. We also present a novel greedy algorithm, our primary contribution, which can be used to solve for a high quality heuristic solution. We present computational analysis to evaluate the performance of our proposed approaches.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Modeling Bilevel Programs in Pyomo

We describe new capabilities for modeling bilevel programs within the Pyomo modeling software. These capabilities include new modeling components that represent subproblems, modeling transformations for re-expressing models with bilevel structure in other forms, and optimize bilevel programs with meta-solvers that apply transformations and then perform op- timization on the resulting model. We illustrate the breadth of Pyomo's modeling capabilities for bilevel programs, and we describe how Pyomo's meta-solvers can perform local and global optimization of bilevel programs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Next-generation Algorithms for Assessing Infrastructure Vulnerability and Optimizing System Resilience

This report summarizes the work performed under the project project Next-Generation Algorithms for Assessing Infrastructure Vulnerability and Optimizing System Resilience. The goal of the project was to improve mathematical programming-based optimization technology for infrastructure protection. In general, the owner of a network wishes to design a network a network that can perform well when certain transportation channels are inhibited (e.g. destroyed) by an adversary. These are typically bi-level problems where the owner designs a system, an adversary optimally attacks it, and then the owner can recover by optimally using the remaining network. This project funded three years of Deon Burchett's graduate research. Deon's graduate advisor, Professor Jean-Philippe Richard, and his Sandia advisors, Richard Chen and Cynthia Phillips, supported Deon on other funds or volunteer time. This report is, therefore. essentially a replication of the Ph.D. dissertation it funded in a format required for project documentation. The thesis had some general polyhedral research. This is the study of the structure of the feasible region of mathematical programs, such as integer programs. For example, an integer program optimizes a linear objective function subject to linear constraints, and (nonlinear) integrality constraints on the variables. The feasible region without the integrality constraints is a convex polygon. Careful study of additional valid constraints can significantly improve computational performance. Here is the abstract from the dissertation: We perform a polyhedral study of a multi-commodity generalization of variable upper bound flow models. In particular, we establish some relations between facets of single- and multi- commodity models. We then introduce a new family of inequalities, which generalizes traditional flow cover inequalities to the multi-commodity context. We present encouraging numerical results. We also consider the directed edge-failure resilient network design problem (DRNDP). This problem entails the design of a directed multi-commodity flow network that is capable of fulfilling a specified percentage of demands in the event that any G arcs are destroyed, where G is a constant parameter. We present a formulation of DRNDP and solve it in a branch-column-cut framework. We present computational results.

97 MATHEMATICS AND COMPUTING↗