Abstract 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.

中文翻译：

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一种估算可再生能源发电厂对 LMP 的结构和行为影响的分析方法

摘要 为了能源安全和减缓全球气候变化，许多国家实施可再生能源支持政策，提高了可再生能源发电厂（REPP）在分析电力市场特征中的重要性。在本文中，介绍了一种分析方法来检查 REPP 对位置边际价格 (LMP) 的影响。为此，考虑到REPPs，根据独立系统运营商（ISO）的社会福利最大化问题，提出了对LMP的新分解，以分析表达传统发电公司（GenCos）和LMP 上的电力系统结构。LMP 的结构部分被分解为四个部分，以区分 GenCos 和 REPPs 的影响。著名的 Q-Learning (QL) 算法对 GenCos 的决策过程进行建模。结果证明 REPP 可以改变 LMP 的结构和行为组成部分，并突出 GenCos 战略行为的重要性。结果表明，在支持政策的设计中不考虑电力系统的特征可能会增加拥塞的可能性或 GenCo 的战略行为的可能性，违背这些政策的目标。