Data-driven stochastic unit commitment considering commercial air conditioning aggregators to provide multi-function demand response

Lingling Le, Jiakun Fang, Menglin Zhang*, Kaiwen Zeng, Xiaomeng Ai, Qiuwei Wu, Jinyu Wen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

This paper proposes a two-stage data-driven unit commitment dispatching scheme, which coordinates the multi-function demand response of commercial air conditioning aggregators with thermal units to encounter the variations of load and wind power. In the proposed dispatching scheme, the flexible ramping capability of the thermal units is reflected in the coupling constraints between the ramping capacity and reserve capacity. To improve the flexibility of the whole system, multi-function demand response of commercial air conditioning aggregators is incorporated into the scheduling in both stages. In the day-ahead stage, load-shifting demand response reduces the ramping requirements by reshaping the load profile. In the real-time stage, reserve-capacity demand response releases the ramping capability by providing reserve for the rebalance of wind power fluctuation, which is represented by a data-driven uncertainty set. Since the dispatching scheme is formulated as a min–max-min problem, an accelerating solution method is developed to improve the computational efficiency, which combines the column-and-constraints generation algorithm and the specific property of the model. The simulation results tested on the modified IEEE 118-bus system and Henan power grid in China validate the effectiveness of the dispatching scheme and solution method in increasing flexible ramping capability, reducing wind power curtailment and load shedding, improving economic efficiency, and accelerating computation.

Original languageEnglish
Article number106790
JournalInternational Journal of Electrical Power and Energy Systems
Volume129
Number of pages12
ISSN0142-0615
DOIs
Publication statusPublished - Jul 2021

Bibliographical note

Funding Information:
This work was supported by Key-Area Research and Development Program of Guangdong Province under Grant no. 2019B111109002 and the National Natural Science Foundation of China (51707077).

Publisher Copyright:
© 2021 Elsevier Ltd

Keywords

  • Accelerating solution method
  • Data-driven uncertainty set
  • Flexible ramping capability
  • Load-shifting demand response
  • Reserve-capacity demand response
  • Stochastic unit commitment

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