Sensitivity analysis of PV output power to capacity configuration of energy storage systems from time and space characteristics

Abstract

Summary The acquisition granularity (time feature quantity) and sampling span (spatial feature quantity) of the data are the feature factors to analyze the active power of renewable energy power stations. According to the time and space characteristics of photovoltaic (PV) power stations, the acquisition granularity and sampling span calibration methods of PV output power based on data mining technology are proposed this paper. The initial range of the acquisition granularity is determined by analyzing the maximum‐order difference components of the PV output power. Through deeply mining the continuous change state of the PV output power, an acquisition granularity calibration method of the PV output power based on the multiobjective optimization model is proposed from time characteristics. The particle swarm optimization algorithm is used to solve the model to obtain the optimal the acquisition granularity of the PV power station. Through the analysis of the sample information entropy change trend of the PV output power, a sampling span calibration method of the PV output power based on the information entropy theory is proposed from space characteristics. The sensitivity analysis of the acquisition granularity and sampling span of the data to the capacity of energy storage systems is realized by the smooth control of the PV output power using first‐order low filters. The simulation tests of the annual history operating data at a PV power station with the installed capacity of 40 MW in China verify the validity of the provided methods. The simulation results show when the acquisition granularity takes 60 seconds and the sampling span takes 33 days, it can satisfy the accuracy of the required data of energy storage systems to realize the smooth control of the PV output power.

Sensitivity Analysis of Time Length of Photovoltaic Output Power to Capacity Configuration of Energy Storage Systems

“…Aiming at a certain PV power plant with an installed capacity of 40 MW, bringing the power data which corresponds to the date time length determined in Section 2 into Equations (11)- (13), according to the calculation method in Section 4.1, when the capacity requirement satisfaction rate is 95%, the storage capacity is 4.4523 MW·h, which represent a 95% probability to meet the daily capacity requirements of the energy storage system, approaching the one-year configured energy storage capacity. Based on the same calculation method, when the installed capacity is 14 MW and 25 MW, we can obtain the corresponding storage capacities, as shown in Table 3.…”

“…In [13], through the analysis of the sample information entropy change trend of the PV output power, a sampling span calibration method for the PV power output based on information entropy theory is proposed based on space characteristics. The simulation results show that the sampling span takes 33 days, which can satisfy the need for accuracy of the required energy storage system data to realize the smooth control of the PV output power.…”

“…Finally, by using the most suitable sample estimation method under typical weather conditions we determine an optimal sampling time length that satisfies the data volume analysis requirements. This paper aims at PV output data of 45 s [25] and 60 s [13] interval to perform the analysis and determine the mathematical statistics. Given a method for determining the time length, then then analyze and verify it through investigating the relationship between the length of PV and the energy storage capacity of a PV power plant.…”