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Participating of micro-grids in energy and spinning reserve markets — Intra-day market

机译:微型电网参与能源和旋转储备市场—日内市场

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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.
机译:由于风力和光伏发电设备的不确定性,该设备在电力市场中的参与会受到重大的偏差罚款。这个问题导致这些单位在竞争环境中感到沮丧甚至被接纳。关于这一事实,即在短期内进行预测时可以使用低偏差,并且分布式发电(DG)单元具有提供辅助服务的若干潜在优势,在本文中,DG单元在市场内的参与辅助服务进行了调查。日内市场包括3-8小时的预定时间,这将导致偏差减少。在这里,将通过使用自适应神经模糊推理系统(ANFIS)来预测三种不确定性,包括可再生DG单元的产量,负荷和电力市场价格。提出的方法通过遗传算法(GA)进行了优化,并在测试系统上进行了测试。结果证明了所提方法的有效性。

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