Document Type: Research Paper
Department of electrical engineering, Birjand university,
Due to uncertain nature of wind and photovoltaic power units, the participation of this units in electricity markets is subjected to significant deviation penalties. This issue leads to despondency or even admission of these units in the competitive environment. With regard to this fact that the low deviations are available when predictions are performed in a short-term horizon and also distributed generation (DG) units have several potential benefits to provide ancillary services, in this article the participation of DG units in intra-market ancillary services is investigated. The intra-day market consists of 3-8 hours scheduled horizon time and will lead to reduction in deviations. Here, three kinds of uncertainties, consist of renewable DG unit’s output, load and price of electricity markets will be predicted by using an adaptive neuro-fuzzy inference system (ANFIS). The proposed method is optimized by Genetic Algorithm (GA) and is tested on a test system. The results supported the efficiency of proposed method.