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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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Gerrit Erichsen & Tobias Zimmermann & Alfons Kather, 2019. "Effect of Different Interval Lengths in a Rolling Horizon MILP Unit Commitment with Non-Linear Control Model for a Small Energy System," Energies, MDPI, vol. 12(6), pages 1-24, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:6:p:1003-:d:214041
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    References listed on IDEAS

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    2. Klemm, Christian & Wiese, Frauke & Vennemann, Peter, 2023. "Model-based run-time and memory reduction for a mixed-use multi-energy system model with high spatial resolution," Applied Energy, Elsevier, vol. 334(C).
    3. Nima Mirzaei Alavijeh & David Steen & Zack Norwood & Le Anh Tuan & Christos Agathokleous, 2020. "Cost-Effectiveness of Carbon Emission Abatement Strategies for a Local Multi-Energy System—A Case Study of Chalmers University of Technology Campus," Energies, MDPI, vol. 13(7), pages 1-23, April.
    4. Harun Or Rashid Howlader & Oludamilare Bode Adewuyi & Ying-Yi Hong & Paras Mandal & Ashraf Mohamed Hemeida & Tomonobu Senjyu, 2019. "Energy Storage System Analysis Review for Optimal Unit Commitment," Energies, MDPI, vol. 13(1), pages 1-21, December.
    5. Jun Hyeok Kang & Jinil Han, 2019. "Optimizing the Operation of Animal Shelters to Minimize Unnecessary Euthanasia: A Case Study in the Seoul Capital Area," Sustainability, MDPI, vol. 11(23), pages 1-13, November.
    6. Marcin Pluta & Artur Wyrwa & Wojciech Suwała & Janusz Zyśk & Maciej Raczyński & Stanisław Tokarski, 2020. "A Generalized Unit Commitment and Economic Dispatch Approach for Analysing the Polish Power System under High Renewable Penetration," Energies, MDPI, vol. 13(8), pages 1-18, April.

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