Unit Commitment With Continuous-Time Generation and Ramping Trajectory Models

作     者：Parvania, Masood Scaglione, Anna 

作者机构：Univ Utah Dept Elect & Comp Engn Salt Lake City UT 84112 USA Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85287 USA 

出 版 物：《IEEE TRANSACTIONS ON POWER SYSTEMS》 (IEEE Trans Power Syst)

年 卷 期：2016年第31卷第4期

页      面：3169-3178页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

基　　金：Department of Energy Consortium for Electric Reliability and Technology National Science Foundation [1549924, 1549923] Directorate For Engineering Div Of Electrical, Commun & Cyber Sys Funding Source: National Science Foundation Directorate For Engineering Div Of Electrical, Commun & Cyber Sys Funding Source: National Science Foundation 

主　　题：Continuous-time function space generation trajectory mixed-integer linear programming ramping trajectory unit commitment 

摘      要：There is increasing evidence of shortage of ramping resources in the real-time operation of power systems. To explain and remedy this problem systematically, in this paper we take a novel look at the way the day-ahead unit commitment (UC) problem represents the information about load, generation and ramping constraints. We specifically investigate the approximation error made in mapping of the original problem, that would decide the continuous-time generation and ramping trajectories of the committed generating units, onto the discrete-time problem that is solved in practice. We first show that current practice amounts to approximating the trajectories with linear splines. We then offer a different representation through cubic splines that provides physically feasible schedules and increases the accuracy of the continuous-time generation and ramping trajectories by capturing sub-hourly variations and ramping of load in the day-ahead power system operation. The corresponding day-ahead UC model is formulated as an instance of mixed-integer linear programming (MILP), with the same number of binary variables as the traditional UC formulation. Numerical simulation over real load data from the California ISO demonstrate that the proposed UC model reduces the total day-ahead and real-time operation cost, and the number of events of ramping scarcity in the real-time operations.