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Tri-stage optimal scheduling for an islanded microgrid based on a quantum adaptive sparrow search algorithm


Li, B and Wang, H and Wang, X and Negnevitsky, M and Li, C, Tri-stage optimal scheduling for an islanded microgrid based on a quantum adaptive sparrow search algorithm, Energy Conversion and Management, 261 pp. 115639. ISSN 0196-8904 (2022) [Refereed Article]

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DOI: doi:10.1016/j.enconman.2022.115639


An islanded microgrid includes a combined heating and power system as well as electric vehicles. It is a key method for increasing energy efficiency, reducing pollution, and achieving carbon neutrality. Considering the uncertainties caused by renewable energy, load, and electric vehicles, this study presents a modified tri-stage scheduling method to realise the real-time dispatch of the islanded microgrid. The novel tri-stage scheduling method incorporates an information feedback mechanism into the traditional tri-stage scheduling method to address the problem of the sub-optimal solution obtained by the traditional method, which results from the significant errors between the day-ahead forecast data and intraday forecast data. In addition, users are guided by the tri-stage real-time price to charge/discharge electric vehicles orderly to fill in the valley and shave the peak of the electric load. In this study, a quantum adaptive sparrow search algorithm is proposed based on the characteristics of the tri-stage scheduling optimisation model and the limitation that the original algorithm easily falls into a local optimum. In the case study, the superiority of the novel tri-stage scheduling method, modified algorithm, and orderly charging/discharging of electric vehicles proposed in this paper are evaluated in different seasons. In the vehicle-to-grid mode, the influence of different numbers of electric vehicles on the real-time dispatch of the islanded microgrid is analyzed and discussed.

Item Details

Item Type:Refereed Article
Keywords:islanded microgrid, electric vehicles, real-time price, tri-stage scheduling method, quantum adaptive sparrow search algorithm
Research Division:Engineering
Research Group:Mechanical engineering
Research Field:Energy generation, conversion and storage (excl. chemical and electrical)
Objective Division:Energy
Objective Group:Energy storage, distribution and supply
Objective Field:Energy systems and analysis
UTAS Author:Wang, X (Professor Xiaolin Wang)
UTAS Author:Negnevitsky, M (Professor Michael Negnevitsky)
UTAS Author:Li, C (Mr Chengjiang Li)
ID Code:149941
Year Published:2022
Deposited By:Engineering
Deposited On:2022-05-02
Last Modified:2022-05-03

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