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

Gerrit Erichsen, Tobias Zimmermann and Alfons Kather
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Gerrit Erichsen: Institute of Energy Systems, Hamburg University of Technology, Denickestr. 15, 21073 Hamburg, Germany
Tobias Zimmermann: Institute of Energy Systems, Hamburg University of Technology, Denickestr. 15, 21073 Hamburg, Germany
Alfons Kather: Institute of Energy Systems, Hamburg University of Technology, Denickestr. 15, 21073 Hamburg, Germany

Energies, 2019, vol. 12, issue 6, 1-24

Abstract: 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 CPLEX ® 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 CO 2 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.

Keywords: mixed integer linear programming; unit commitment; rolling horizon; islands; non-linear control; MILP models; renewable energies; long-term storage (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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