An interactive multi-criteria decision-making framework between a renewable power plant planner and the independent system operator

• Type: Article in International Scientific Journal
• Date: 2021

Authors

Salman Soltaniyan (Author) Other; Mohammad Reza Salehizadeh (Author) Other; Akin Tascikaraoglu (Author) Other; Ozan Erdinç (Author) Other; João P. S. Catalão (Author) FEUP

Journal

Title: Sustainable Energy, Grids and Networks

Vol. 26

Pages: 1-11

ISSN: 2352-4677

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 4 Citations

Scopus - 4 Citations

Other information

Authenticus ID: P-00T-H07

DOI: 10.1016/j.segan.2021.100447

Abstract (EN): Providing efficient support mechanisms for renewable energy promotion has drawn much attention from researchers in the recent years. The connection of a new renewable power plant to the transmission system has impacts on different electricity market indices since the other strategic generation units change their behaviour in the new multi-agent environment. In this paper, as the main contribution to the previous literature, a combination of multi-criteria decision-making approach and multi-agent modelling technique is developed to obtain the maximum possible profits for an intended renewable generation plan and also direct the investment to be located in a way to improve electricity market indices besides supporting renewable energy promotion. Fuzzy Q-learning electricity market modelling approach in combination with the technique for order preference by similarity (TOPSIS) is used as a new decision support system for promotion of renewable energy for the first time in the literature. The proposed interactive multi-criteria decision-making framework between the independent system operator (ISO) and the renewable power plant planner provides a win win situation that improve market indices while help the renewable power plant planning. The effectiveness of the proposed method is examined on the IEEE 30-bus test system and the results are discussed.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11