Decomposing Optimization-Based Bounds Tightening Problems Via Graph Partitioning
Abstract not provided.
Publications
SECURE: An Evidence-based Approach to Cyber Experimentation
Proceedings - 2019 Resilience Week, RWS 2019
Securing cyber systems is of paramount importance, but rigorous, evidence-based techniques to support decision makers for high-consequence decisions have been missing. The need for bringing rigor into cybersecurity is well-recognized, but little progress has been made over the last decades. We introduce a new project, SECURE, that aims to bring more rigor into cyber experimentation. The core idea is to follow the footsteps of computational science and engineering and expand similar capabilities to support rigorous cyber experimentation. In this paper, we review the cyber experimentation process, present the research areas that underlie our effort, discuss the underlying research challenges, and report on our progress to date. This paper is based on work in progress, and we expect to have more complete results for the conference.
EGRET: Electrical Grid Research and Engineering Tools
Abstract not provided.
EGRET: Unit Commitment and {Economic?} Dispatch
Abstract not provided.
Decomposing Optimization Based Bounds Tightening Problems Via Graph Partitioning
Abstract not provided.
Evaluating demand response opportunities for power systems resilience using MILP and MINLP Formulations
AIChE Journal
While peak shaving is commonly used to reduce power costs, chemical process facilities that can reduce power consumption on demand during emergencies (e.g., extreme weather events) bring additional value through improved resilience. For process facilities to effectively negotiate demand response (DR) contracts and make investment decisions regarding flexibility, they need to quantify their additional value to the grid. We present a grid-centric mixed-integer stochastic programming framework to determine the value of DR for improving grid resilience in place of capital investments that can be cost prohibitive for system operators. We formulate problems using both a linear approximation and a nonlinear alternating current power flow model. Our numerical results with both models demonstrate that DR can be used to reduce the capital investment necessary for resilience, increasing the value that chemical process facilities bring through DR. However, the linearized model often underestimates the amount of DR needed in our case studies. Published 2018. This article is a U.S. Government work and is in the public domain in the USA. AIChE J, 65: e16508, 2019.
Mixed-Integer Nonlinear Programming Applications: Pyomo Tools and Tailored Strategies
Abstract not provided.
Global Solution Strategies for the Network-Constrained Unit Commitment Problem with AC Transmission Constraints
IEEE Transactions on Power Systems
We propose a novel global solution algorithm for the network-constrained unit commitment problem that incorporates a nonlinear alternating current (ac) model of the transmission network, which is a nonconvex mixed-integer nonlinear programming problem. Our algorithm is based on the multi-tree global optimization methodology, which iterates between a mixed-integer lower-bounding problem and a nonlinear upper-bounding problem. We exploit the mathematical structure of the unit commitment problem with ac power flow constraints and leverage second-order cone relaxations, piecewise outer approximations, and optimization-based bounds tightening to provide a globally optimal solution at convergence. Numerical results on four benchmark problems illustrate the effectiveness of our algorithm, both in terms of convergence rate and solution quality.
Improving Power Systems Resilience with MILP and MINLP Formulations
Abstract not provided.
Strengthening Optimal Power Flow Relaxations with Reference Bus Constraints and Bounds Tightening
Abstract not provided.
A multitree approach for global solution of ACOPF problems using piecewise outer approximations
Computers and Chemical Engineering
Electricity markets rely on the rapid solution of the optimal power flow (OPF) problem to determine generator power levels and set nodal prices. Traditionally, the OPF problem has been formulated using linearized, approximate models, ignoring nonlinear alternating current (AC) physics. These approaches do not guarantee global optimality or even feasibility in the real ACOPF problem. We introduce an outer-approximation approach to solve the ACOPF problem to global optimality based on alternating solution of upper- and lower-bounding problems. The lower-bounding problem is a piecewise relaxation based on strong second-order cone relaxations of the ACOPF, and these piecewise relaxations are selectively refined at each major iteration through increased variable domain partitioning. Our approach is able to efficiently solve all but one of the test cases considered to an optimality gap below 0.1%. Furthermore, this approach opens the door for global solution of MINLP problems with AC power flow equations.
Global Optimization for AC Optimal Power Flow Applications
Abstract not provided.
Global Solution of AC Optimal Power Flow Problems
Abstract not provided.
Strengthened SOCP Relaxations for ACOPF with McCormick Envelopes and Bounds Tightening
Computer Aided Chemical Engineering
The solution of the Optimal Power Flow (OPF) and Unit Commitment (UC) problems (i.e., determining generator schedules and set points that satisfy demands) is critical for efficient and reliable operation of the electricity grid. For computational efficiency, the alternating current OPF (ACOPF) problem is usually formulated with a linearized transmission model, often referred to as the DCOPF problem. However, these linear approximations do not guarantee global optimality or even feasibility for the true nonlinear alternating current (AC) system. Nonlinear AC power flow models can and should be used to improve model fidelity, but successful global solution of problems with these models requires the availability of strong relaxations of the AC optimal power flow constraints. In this paper, we use McCormick envelopes to strengthen the well-known second-order cone (SOC) relaxation of the ACOPF problem. With this improved relaxation, we can further include tight bounds on the voltages at the reference bus, and this paper demonstrates the effectiveness of this for improved bounds tightening. We present results on the optimality gap of both the base SOC relaxation and our Strengthened SOC (SSOC) relaxation for the National Information and Communications Technology Australia (NICTA) Energy System Test Case Archive (NESTA). For the cases where the SOC relaxation yields an optimality gap more than 0.1 %, the SSOC relaxation with bounds tightening further reduces the optimality gap by an average of 67 % and ultimately reduces the optimality gap to less than 0.1 % for 58 % of all the NESTA cases considered. Stronger relaxations enable more efficient global solution of the ACOPF problem and can improve computational efficiency of MINLP problems with AC power flow constraints, e.g., unit commitment.
Stochastic Optimization to Enhance Resiliency and Response Strategies in Critical Infrastructure
Abstract not provided.
Solving the Mixed Integer Non-linear Programming Problem of Unit Commitment
Abstract not provided.
Solving the MINLP Problem of Unit Commitment on AC Power Systems
Abstract not provided.
17 Results