An Interactive Multi-Criteria Decision-Making Framework between a Renewable Power Plant Planner and the Independent System Operator Salman Soltaniyan 1 , Mohammad Reza Salehizadeh 2,* , Akın Taşcıkaraoğlu 3 , Ozan Erdinç 4 , and João P. S. Catalão 5,* 1 Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran 2 Department of Electrical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran 3 Department of Electrical and Electronics Engineering, Mugla Sitki Kocman University, Mugla, Turkey 4 Department of Electrical Engineering, Yildiz Technical University, Istanbul, Turkey 5 Faculty of Engineering of the University of Porto and INESC TEC, Porto 4200-465, Portugal * salehizadeh@miau.ac.ir, catalao@fe.up.pt Abstract 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. Keywords: Electricity Market; Power System; Renewable Energy; Reinforcement Learning; Fuzzy Q-learning.