Storage and Delivery Constrained Unit Commitment

A general formulation and the development of a dynamic programming algorithm to solve a fuel-constrained unit commitment problem is presented. The system under consideration has oil units with storage constraints, gas units with delivery constraints, and unconstrained coal units. An optimal approach to transfer the fuel delivery constraints into unit capacity limits using a closed-form dual dispatch is introduced. As a result, the gas units can be viewed as fuel-unconstrained, thus greatly reducing complexity. It is shown that the optimization problem, which requires that storage levels be parameterized, leads to two different dispatch rules. The oil units are dispatched to maximize the power they generate for a binding fuel amount or cost, whereas the gas and coal units are economically dispatched to provide for the remaining power. In addition to the standard constraints, the system is required to meet a minimum system spinning reserve. Test results are provided to illustrate the merits of the proposed metho

Storage and Delivery Constrained Unit Commitment

A general formulation and the development of a dynamic programming algorithm to solve a fuel-constrained unit commitment problem is presented. The system under consideration has oil units with storage constraints, gas units with delivery constraints, and unconstrained coal units. An optimal approach to transfer the fuel delivery constraints into unit capacity limits using a closed-form dual dispatch is introduced. As a result, the gas units can be viewed as fuel-unconstrained, thus greatly reducing complexity. It is shown that the optimization problem, which requires that storage levels be parameterized, leads to two different dispatch rules. The oil units are dispatched to maximize the power they generate for a binding fuel amount or cost, whereas the gas and coal units are economically dispatched to provide for the remaining power. In addition to the standard constraints, the system is required to meet a minimum system spinning reserve. Test results are provided to illustrate the merits of the proposed metho

Integration of Distance Learning Technology into Traditional Engineering Physical Laboratory Exercises

This paper was published in the Proc. of the ASEE SE Section Regional Conference. The use of distance learning technology in distributed educational environments has allowed engineering courses to be delivered to locations and populations that have historically not been afforded opportunities for involvement. However, efforts to incorporate distance-learning principles into physical laboratory exercises have not led to a general mechanism or procedure for performing physical labs remotely. Removing these exercises from the course is not a satisfactory solution since physical laboratory exercises are a vital component of any educational curriculum in virtually every major field of engineering. Recently, faculty members from three universities in south Georgia have teamed up to collaboratively develop an innovative approach to integrate distance learning technology into traditional engineering physical laboratory exercises. An overview of this project and findings related to its initial phase are presented in this paper

Large-scale unit commitment under uncertainty: an updated literature survey

The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject