A new converged Emperor Penguin Optimizer for biding strategy in a day-ahead deregulated market clearing price: A case study in China

Xiaohui Lu, Yang Yang, Peifang Wang, Yiming Fan, Fangzhong Yu and Nicholas Zafetti

Energy, 2021, vol. 227, issue C

Abstract: In this research, a new approach has been suggested for providing an optimization bidding strategy in the day-ahead market for case research in China. This research uses a newly developed version of Emperor Penguin Optimizer (CEPO) to govern the fitness function of all individuals based on the Market Clearing Price (MCP) probability function. The resemblance amounts between each day and the next day are used for clustering. The clustering is performed based on the well-known subtractive clustering methodology. A simulation model using the probability function in MCP estimates the fitness function of the generated strategies. The results indicate that this method proposed is a statistically effective bidding design in China’s day-ahead market related to two other plans from the literature.

Keywords: Market clearing price; Resemblance value; Subtractive clustering; Converged emperor penguin optimizer; Sequential quadratic programming (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544221006356
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:227:y:2021:i:c:s0360544221006356

DOI: 10.1016/j.energy.2021.120386

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().