Distributionally robust optimal scheduling for multi-energy with uncertainties

Pengfei Zhang; Zenghui Xi; Guo Chuangxin

doi:10.1117/12.3015834

6 February 2024 Distributionally robust optimal scheduling for multi-energy with uncertainties

Pengfei Zhang, Zenghui Xi, Guo Chuangxin

Proceedings Volume 12979, Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023); 129792W (2024) https://doi.org/10.1117/12.3015834
Event: 9th International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023), 2023, Guilin, China

Abstract

Under the background of the energy revolution, the new power system is gradually transforming into the energy Internet. With the large-scale penetration of wind power and the diversification of service modes of regional integrated energy systems, the uncertainty of source and load has become a difficult problem in the planning and operation of large-scale integrated energy systems. Aiming at the above problems, this paper proposes a highly general distribution-integrated integrated energy system distribution robust scheduling method. First, the large-scale new IES is divided into electric-gas transmission network and multi-energy flow energy hub distribution network. The power-gas-heat-hydrogen system's distribution and transmission process is finely modeled; then, an uncertain set of wind power and multi-energy flow loads based on discrete scene sets of historical data is constructed, and a data-driven comprehensive energy system with integrated transmission and distribution min -max-min two-stage distribution robust optimization model; Finally, the non convex airflow equation is transformed into a second-order cone form, and the original MINLP problem is transformed into a MISOCP problem. After dual transformation, the overall model is solved using the SOC-C&CG method.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Pengfei Zhang, Zenghui Xi, and Guo Chuangxin "Distributionally robust optimal scheduling for multi-energy with uncertainties", Proc. SPIE 12979, Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023), 129792W (6 February 2024); https://doi.org/10.1117/12.3015834

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