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Enhancing operation flexibility of distributed energy systems : a flexible multi-objective optimization planning method considering long-term and temporary objectives

  • Xiaoyuan Li
  • , Zhe Tian
  • , Jide Niu*
  • , Xiaolei Yuan
  • , Risto Kosonen
  • , Rami Niemi
  • , Bin Yang
  • *Corresponding author for this work

Research output: Contribution to journalConference articleScientificpeer-review

Abstract

Flexibility is a crucial capability that distributed energy systems (DES) need to possess. Flexible DES can attain the objectives of daily economic operation for users, temporary demand response for the power grid, and low-carbon operation for environmental benefits. Among these objectives, economy and low-carbon objectives are long-term objectives that need to be met routinely, while grid-friendly interactions only occur temporarily but need to be met as a priority. Current multi-objective optimization planning models disregard the inherent differences in temporal property of these objectives, which may weaken the system’s flexibility. Here, a novel multi-objective optimization model is proposed to enhance the flexibility of DES. First, a new flexibility index is formulated, in which diverse objectives are evaluated synergistically across different scenarios. Second, a flexible mixed-integer multi-objective programming model is developed, which returns the most flexible system design and its operation strategies for each objective. The application in a real case in Tianjin, China revealed that, compared with the conventional multi-objective planning method, the proposed method could improve the system flexibility by 6%-10%. The universality of the proposed method was further verified by traversal analysis.

Original languageEnglish
Pages (from-to)3099-3106
Number of pages8
JournalBuilding Simulation Conference Proceedings
Volume18
DOIs
Publication statusPublished - 2023
MoE publication typeA4 Conference publication
EventIBPSA Building Simulation Conference - Shanghai, China
Duration: 4 Sept 20236 Sept 2023
Conference number: 18

Funding

This work is supported by National Natural Science Foundation for Young Scientists of China (No. 52108085). References Akbari, K., M. M. Nasiri, F. Jolai, et al. (2014). Optimal investment and unit sizing of distributed energy systems under uncertainty: A robust optimization approach. Energy and Buildings 85, 275-286.

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