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

机译:参与能源和纺纱储备市场的微观网格 - 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:由于风和光伏电量的不确定性质,这种单位在电力市场中的参与受到显着的偏差惩罚。这个问题导致竞争环境中的沮丧甚至录取这些单位。关于这一事实,当在短期地平线中进行预测并且还具有分布式发电(DG)单位时,低偏差有几个潜在的好处,可以在本文中提供辅助服务的潜在利益,DG单位参与市场调查了辅助服务。内部市场由3-8小时的计划定期时间组成,将导致偏差减少。这里,通过使用自适应神经模糊推理系统(ANFIS)预测三种不确定性,由可再生DG单元的输出,负载和电力市场的价格预测。所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。结果支持提出方法的效率。

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