Implementation of renewable energy support policies in many countries for the sake of energy security and climate change mitigation worldwide raises the importance of renewable energy power plants (REPPs) in analyzing power market features. In this article, an analytical method is introduced to examine the effect of REPPs on locational marginal price (LMP). To this end, taking into account REPPs, according to the social welfare maximization problem of the independent system operator (ISO), a new decomposition for LMP is proposed to analytically express the impact of the strategic behavior of conventional generation companies (GenCos) and the power system structure on LMP. The structural section of LMP is decomposed into four components to differentiate between the effect of GenCos and REPPs. The well-known Q-Learning (QL) algorithm models the GenCos' decision-making process. The results prove that REPPs can change both the structural and behavioral components of the LMP and highlight the significance of GenCos' strategic behavior. The results demonstrate that not considering the features of the power system in the design of support policies might raise the likelihood of congestion or the probability of strategic behaviors from GenCos, against the objectives of these policies. (C) 2020 Published by Elsevier Ltd.