Operations Planning Experiments for Power Systems with High Renewable Resources

Driven by ambitious renewable portfolio standards, variable energy resources (such as wind and solar) are expected to impose unprecedented levels of uncertainty to power system operations. The current practice of planning operations with deterministic optimization tools may be ill-suited for a future where uncertainty is abundant. To overcome the reliability challenges associated with the large-scale … Read more

A Progressive Hedging Based Branch-and-Bound Algorithm for Stochastic Mixed-Integer Programs

Progressive Hedging (PH) is a well-known algorithm for solving multi-stage stochastic convex optimization problems. Most previous extensions of PH for stochastic mixed-integer programs have been implemented without convergence guarantees. In this paper, we present a new framework that shows how PH can be utilized while guaranteeing convergence to globally optimal solutions of stochastic mixed-integer convex … Read more

A State Transition MIP Formulation for the Unit Commitment Problem

In this paper, we present the state-transition formulation for the unit commitment problem. This formulation is based on the definition of new decision variables, which, instead of indicating the on/off statuses of a generator, captures its state transitions between consecutive time periods. We show that this new approach produces a formulation which naturally includes valid … Read more

A Stochastic Optimization Model for Designing Last Mile Relief Networks

In this study, we introduce a distribution network design problem that determines the locations and capacities of the relief distribution points in the last mile network, while considering demand- and network-related uncertainties in the post-disaster environment. The problem addresses the critical concerns of relief organizations in designing last mile networks, which are providing accessible and … Read more

Minimizing Value-at-Risk in Single-Machine Scheduling

The vast majority of the machine scheduling literature focuses on deterministic problems in which all data is known with certainty a priori. In practice, this assumption implies that the random parameters in the problem are represented by their point estimates in the scheduling model. The resulting schedules may perform well if the variability in the … Read more