Yongpei Guan In this errata, we corrected the imprecise statements in “Polynomial Time Algorithms and Extended Formulations for Unit Commitment Problems” [IISE Transactions 50 (8): 735-751, 2018]. Article Download View Errata to "Polynomial Time Algorithms and Extended Formulations for Unit Commitment Problems"
Due to the increased utilization of gas-fired combined-cycle units for power generation in the U.S., accurate and computationally efficient models are more and more needed. The recently proposed edge-based formulation for combined-cycle units helps accurately describe the operations of combined-cycle units including capturing the transition processes and physical constraints for each turbine. In this paper, … Read more
Recently increasing penetration of renewable energy generation brings challenges for power system operators to perform efficient power generation daily scheduling, due to the intermittent nature of the renewable generation and discrete decisions of each generation unit. Among all aspects to be considered, unit commitment polytope is fundamental and embedded in the models at different stages … Read more
In this paper, we study a two-level lot-sizing problem with supplier selection (LSS). This NP-hard problem arises in different production planning and supply chain management applications. We first present a dynamic programming algorithm for LSS that is polynomial when the number of plants is fixed. We use this algorithm to describe the convex hull of … Read more
In this paper, we consider the polyhedral structure of the integrated minimum-up/-down time and ramping polytope, which has broad applications in power generation scheduling problems. The generalized polytope we studied includes minimum-up/-down time, generation ramp-up/-down rate, logical, and generation upper/lower bound constraints. We derive strong valid inequalities for this polytope by utilizing its specialized structures. … Read more
The traditional two-stage stochastic programming approach assumes the distribution of the random parameter in a problem is known. In most practices, however, the distribution is actually unknown. Instead, only a series of historic data are available. In this paper, we develop a data-driven stochastic optimization approach to providing a risk-averse decision making under uncertainty. In … Read more
In this paper, we consider the polyhedral structure of the integrated minimum-up/-down time and ramping polytope for the unit commitment problem. Our studied generalized polytope includes minimum-up/-down time constraints, generation ramp-up/-down rate constraints, logical constraints, and generation upper/lower bound constraints. We derive strong valid inequalities by utilizing the structures of the unit commitment problem, and … Read more
In this paper, we develop a risk-averse two-stage stochastic program (RTSP) which explicitly incorporates the distributional ambiguity covering both discrete and continuous distributions. Starting from a set of historical data samples, we construct a confidence set for the ambiguous probability distribution through nonparametric statistical estimation of its density function. We then formulate RTSP from the … Read more
The traditional two-stage stochastic program approach is to minimize the total expected cost with the consideration of parameter uncertainty, and the distribution of the random parameters is assumed to be known. However, in most practices, the actual distribution of the random parameters is not known, and only a certain amount of historical data are available. … Read more
In traditional thinking, an arbitrageur will trade immediately once an arbitrage opportunity appears. Is this the best strategy for the arbitrageur or it is even better to wait for the best time to trade so as to achieve the maximum pro fit? To answer this question, this paper studies the optimal trading strategies of an arbitrageur … Read more