Effect of Different Interval Lengths in a Rolling Horizon MILP Unit Commitment with Non-Linear Control Model for a Small Energy System

作     者：Erichsen, Gerrit Zimmermann, Tobias Kather, Alfons 

作者机构：Hamburg Univ Technol Inst Energy Syst Denickestr 15 D-21073 Hamburg Germany 

出 版 物：《ENERGIES》 (能源)

年 卷 期：2019年第12卷第6期

页      面：1003-1003页

核心收录：

学科分类：0820[工学-石油与天然气工程] 08[工学] 0807[工学-动力工程及工程热物理] 

基　　金：Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Hamburg University of Technology (TUHH) 

主　　题：mixed integer linear programming unit commitment rolling horizon islands non-linear control MILP models renewable energies long-term storage 

摘      要：In this paper, a fixed electricity producer park of both a short- and long-term renewable energy storage (e.g., battery, power to gas to power) and a conventional power plant is combined with an increasing amount of installed volatile renewable power. For the sake of simplicity, the grid is designed as a single copper plate with island restrictions and constant demand of 1000 MW;the volatile input is deducted from scaled 15-min input data of German grid operators. A mixed integer linear programming model is implemented to generate an optimised unit commitment (UCO) for various scenarios and configurations using CPLEXA (R) as the problem solver. The resulting unit commitment is input into a non-linear control model (NLC), which tries to match the plan of the UCO as closely as possible. Using the approach of a rolling horizon the result of the NLC is fed back to the interval of the next optimisation run. The problem s objective is set to minimise CO2 emissions of the whole electricity producer park. Different interval lengths are tested with perfect foresight. The results gained with different interval lengths are compared to each other and to a simple heuristic approach. As non-linear control model a characteristic line model is used. The results show that the influence of the interval length is rather small, which leads to the conclusion that realistic forecast lengths of two days can be used to achieve not only a sufficient quality of solutions, but shorter computational times as well.