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Effect of different interval lengths in a rolling horizon MILP unit commitment with non-linear control model for a small energy system
Citation Link: https://doi.org/10.15480/882.2117
Publikationstyp
Journal Article
Date Issued
2019-03-14
Sprache
English
Author(s)
Institut
TORE-DOI
TORE-URI
Journal
Volume
12
Issue
6
Start Page
Art.-Nr. 1003
Citation
Energies 12 (6): 1003 (2019)
Publisher DOI
Scopus ID
Publisher
Multidisciplinary Digital Publishing Institute
Peer Reviewed
true
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 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.
Subjects
mixed integer linear programming
unit commitment
rolling horizon
slands
nonlinear control
MILP models
renewable energies
long-term storage
DDC Class
500: Naturwissenschaften
600: Technik
620: Ingenieurwissenschaften
Publication version
publishedVersion
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