Different Terms Fluctuations of Solar Power System

The generation of solar power are known to be seriously influenced by many factors, such as temperature, humidity, et al. These factors cause the fluctuations of solar power system. This paper considers those fluctuations of different terms by using actual historical data and the different distribution method. From the analysis and simulations, we find the fluctuation data for different terms can fit different kinds of distribution functions, such Gaussian distribution, T Location-scale, Histogram distribution, and Logistic distribution. Finally, the simulation shows the method is effective and efficient. Introduction Nowadays, the utilization of renewable energy in the power system has deeply developed. The solar system has a priority. For example, these renewable energies will share of 20% of the total energy consumption by 2020 and 60 % by 2050 [1]. As knows, these renewable sources are highly fluctuating for different time scales, see [2,3] and reference therein. For safety reason, we must handle the situation and forecast the fluctuations and control them. In traditional power plants, the inertia of fast rotating generators is utilized as an automatic power reserve. This is done simply by speeding up or slowing down the rotating masses, keeping the grid frequency within a narrow range around the nominal frequency. In the ENTSO-E ‡ grid, the value of the nominal frequency is 50 Hz and the tolerated deviation from this value is ±10 MHz [4]. Restoring the grid frequency to the nominal frequency, in current practice, is provided by traditional frequency control, which has three categories: primary, secondary and tertiary frequency control, cf. [5]. The primary frequency control is provided within a few seconds after the occurrence of a frequency deviation. It provides extra power for stabilizing the system frequency (but not restoring it to the nominal frequency [6]. The secondary frequency control acts after approximately 30 s and restores both the grid frequency from its residual deviation and the corresponding tie-line power exchanges with other control zones to the set-point values. Tertiary frequency control manually adapts power generation and load set-points and controls the grid operation beyond the initial 15 minutes’ time-frame after a fault event has occurred. In the background of replacing the successively controllable conventional power plants by intermittent renewable power systems, there are several recent works studying the grid stability under these new constraints [7-9]. One practical approach is that synchronous machines of old power plants are still connected to the grid and providing the reactive power and inertia [10]. It has also been a practical topic to study how the stability of the power grid can be kept in the lower rotational inertia case (because of high penetration of renewable sources) using some faster control reserves [11,12]. One possible option is to use battery storage providing primary control reserve, see e.g. [13] for a very recent study on this topic. PV output is affected by weather severely [1], when cloudy or overcast weather, the PV output will appear violent fluctuations. [2-5] points out that the fluctuation of PV output is harmful to the safety and stability of power grid. [6-8] propose that using energy storage equipment, the output fluctuation of photovoltaic or wind power can be smoothed by a certain control strategy. Literature [9] comes to the conclusion that when PV output appear large climbing rate, the irradiance curve 4th International Conference on Machinery, Materials and Information Technology Applications (ICMMITA 2016) Copyright © 2017, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Advances in Computer Science Research, volume 71