Application of fuzzy Q-learning for electricity market modeling by considering renewable power penetration

Mohammad Reza Salehizadeh and Salman Soltaniyan

Renewable and Sustainable Energy Reviews, 2016, vol. 56, issue C, 1172-1181

Abstract: By increasing renewable resource penetration, the need for developing fast and reliable market modeling approaches in the presence of these resources has gained greater attention. In this paper, fuzzy Q-learning approach is proposed for hour-ahead electricity market modeling in presence of renewable resources. The proposed approach is implemented on IEEE 30-bus test system. The effectiveness of the proposed approach is evaluated and compared with Q-learning approach for both normal and stressful cases. Simulation results indicate that the proposed approach is able to model electricity market for a range of continuous multidimensional renewable power penetration in considerably less iterations compared with Q-learning approach. Moreover, the probability of finding Nash equilibrium is becoming higher by using fuzzy Q-learning approach, while the other indices such as average social welfare, average of locational marginal prices (LMPs), and average standard of deviation of LMPs do not change considerably.

Keywords: Agent-based computational modeling; Electricity market; Fuzzy Q-learning; Multi-agent system; Nash equilibrium; Renewable power penetration; Supply function (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (7)

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DOI: 10.1016/j.rser.2015.12.020

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