Publications

Laird, Carl D.; Watson, Jean-Paul W.; Hart, William E.; Siirola, John D.; Nicholson, Bethany L.

Abstract not provided.

INFORMS Journal on Computing

Knueven, Ben; Ostrowski, James; Watson, Jean-Paul W.

We provide a comprehensive overview of mixed-integer programming formulations for the unit commitment (UC) problem. UC formulations have been an especially active area of research over the past 12 years due to their practical importance in power grid operations, and this paper serves as a capstone for this line of work. We additionally provide publicly available reference implementations of all formulations examined. We computationally test existing and novel UC formulations on a suite of instances drawn from both academic and real-world data sources. Driven by our computational experience from this and previous work, we contribute some additional formulations for both generator production upper bounds and piecewise linear production costs. By composing new UC formulations using existing components found in the literature and new components introduced in this paper, we demonstrate that performance can be significantly improved—and in the process, we identify a new state-of-the-art UC formulation.

IEEE Power and Energy Society General Meeting

Wilches-Bernal, Felipe; Knueven, Ben; Staid, Andrea S.; Watson, Jean-Paul W.

This paper presents model formulations for generators that have the ability to use multiple fuels and to switch between them if necessary. These models are used to generate different scenarios of fuel switching penetration from a test power system. With these scenarios, for a severe disruption in the fuel supply to multiple generators, the paper analyzes the effect that fuel switching has on the resilience of the power system. Load not served is used as the proxy metric to evaluate power system resilience. The paper shows that the presence of generators with fuel switching capabilities considerably reduces the amount and duration of the load shed by the system facing the fuel disruption.

Knueven, Ben; Bynum, Michael L.; Castillo, Anya; Laird, Carl D.; Watson, Jean-Paul W.

Abstract not provided.

Watson, Jean-Paul W.; Knueven, Ben; Ostrowski, Jim &.

Abstract not provided.

Knueven, Ben; Bynum, Michael L.; Castillo, Anya; Laird, Carl D.; Watson, Jean-Paul W.

Abstract not provided.

Optimization Letters

Singh, Bismark S.; Watson, Jean-Paul W.

We consider a joint-chance constraint (JCC) as a union of sets, and approximate this union using bounds from classical probability theory. When these bounds are used in an optimization model constrained by the JCC, we obtain corresponding upper and lower bounds on the optimal objective function value. We compare the strength of these bounds against each other under two different sampling schemes, and observe that a larger correlation between the uncertainties tends to result in more computationally challenging optimization models. We also observe the same set of inequalities to provide the tightest upper and lower bounds in our computational experiments.

AIChE Journal

Bynum, Michael L.; Castillo, Anya; Watson, Jean-Paul W.; Laird, Carl D.

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.

Singh, Bismark S.; Knueven, Ben; Watson, Jean-Paul W.

Abstract not provided.

Laird, Carl D.; Bynum, Michael L.; Castillo, Anya; Watson, Jean-Paul W.; Liu, Jianfeng L.

Abstract not provided.

IEEE Transactions on Power Systems

Liu, Jianfeng; Laird, Carl D.; Scott, Joseph K.; Watson, Jean-Paul W.; Castillo, Anya

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.

Bynum, Michael L.; Castillo, Anya; Laird, Carl D.; Watson, Jean-Paul W.

Abstract not provided.

Knueven, Ben; Ostrowski, James O.; Watson, Jean-Paul W.

Abstract not provided.

Bynum, Michael L.; Laird, Carl D.; Castillo, Anya; Watson, Jean-Paul W.

Abstract not provided.

Knueven, Ben; Ostrowski, James O.; Watson, Jean-Paul W.

Abstract not provided.

Singh, Bismark S.; Watson, Jean-Paul W.

Abstract not provided.

Singh, Bismark S.; Watson, Jean-Paul W.

Abstract not provided.

2018 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2018 - Proceedings

Rachunok, Benjamin A.; Staid, Andrea S.; Watson, Jean-Paul W.; Woodruff, David L.; Yang, Dominic

Stochastic versions of the unit commitment problem have been advocated for addressing the uncertainty presented by high levels of wind power penetration. However, little work has been done to study trade-offs between computational complexity and the quality of solutions obtained as the number of probabilistic scenarios is varied. Here, we describe extensive experiments using real publicly available wind power data from the Bonneville Power Administration. Solution quality is measured by re-enacting day-ahead reliability unit commitment (which selects the thermal units that will be used each hour of the next day) and real-time economic dispatch (which determines generation levels) for an enhanced WECC-240 test system in the context of a production cost model simulator; outputs from the simulation, including cost, reliability, and computational performance metrics, are then analyzed. Unsurprisingly, we find that both solution quality and computational difficulty increase with the number of probabilistic scenarios considered. However, we find unexpected transitions in computational difficulty at a specific threshold in the number of scenarios, and report on key trends in solution performance characteristics. Our findings are novel in that we examine these tradeoffs using real-world wind power data in the context of an out-of-sample production cost model simulation, and are relevant for both practitioners interested in deploying and researchers interested in developing scalable solvers for stochastic unit commitment.

IEEE Transactions on Power Systems

Knueven, Ben; Ostrowski, Jim; Watson, Jean-Paul W.

We present sufficient conditions under which thermal generators can be aggregated in mixed-integer linear programming (MILP) formulations of the unit commitment (UC) problem, while maintaining feasibility and optimality for the original disaggregated problem. Aggregating thermal generators with identical characteristics (e.g., minimum/maximum power output, minimum up/down time, and cost curves) into a single unit reduces redundancy in the search space induced by both exact symmetry (permutations of generator schedules) and certain classes of mutually nondominated solutions. We study the impact of aggregation on two large-scale UC instances: one from the academic literature and the other based on real-world operator data. Our computational tests demonstrate that, when present, identical generators can negatively affect the performance of modern MILP solvers on UC formulations. Furthermore, we show that our reformation of the UC MILP through aggregation is an effective method for mitigating this source of computational difficulty.

Computers and Chemical Engineering

Liu, Jianfeng; Bynum, Michael L.; Castillo, Anya; Watson, Jean-Paul W.; Laird, Carl D.

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.

Singh, Bismark S.; Watson, Jean-Paul W.

Abstract not provided.

Arguello, Bryan A.; Hart, William E.; Watson, Jean-Paul W.

Abstract not provided.

Singh, Bismark S.; Watson, Jean-Paul W.

Abstract not provided.

Rachunok, Benjamin A.; Staid, Andrea S.; Watson, Jean-Paul W.; Woodruff, David L.; Yang, Dominic

Abstract not provided.

Castillo, Anya; Watson, Jean-Paul W.; Bynum, Michael L.; Laird, Carl D.

Abstract not provided.