Low-carbon based multi-objective bi-level power dispatching under uncertainty

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

12 Scopus Citations
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Author(s)

  • Xiaoyang Zhou
  • Canhui Zhao
  • Jian Chai
  • Benjamin Lev
  • Kin Keung Lai

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number533
Journal / PublicationSustainability (Switzerland)
Volume8
Issue number6
Publication statusPublished - 4 Jun 2016

Link(s)

Abstract

This research examines a low-carbon power dispatch problem under uncertainty. A hybrid uncertain multi-objective bi-level model with one leader and multiple followers is established to support the decision making of power dispatch and generation. The upper level decision maker is the regional power grid corporation which allocates power quotas to each follower based on the objectives of reasonable returns, a small power surplus and low carbon emissions. The lower level decision makers are the power generation groups which decide on their respective power generation plans and prices to ensure the highest total revenue under consideration of government subsidies, environmental costs and the carbon trading. Random and fuzzy variables are adopted to describe the uncertain factors and chance constrained and expected value programming are used to handle the hybrid uncertain model. The bi-level models are then transformed into solvable single level models using a satisfaction method. Finally, a detailed case study and comparative analyses are presented to test the proposed models and approaches to validate the effectiveness and illustrate the advantages.

Research Area(s)

  • Carbon trading, Chance constrained programming, Expected value programming, Hybrid uncertain multi-objective bi-level model, Low carbon, Power dispatching, Satisfaction method

Citation Format(s)

Low-carbon based multi-objective bi-level power dispatching under uncertainty. / Zhou, Xiaoyang; Zhao, Canhui; Chai, Jian et al.

In: Sustainability (Switzerland), Vol. 8, No. 6, 533, 04.06.2016.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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