Daily Clearness Index Profiles Cluster Analysis for Photovoltaic System

Abstract

Due to various weather perturbation effects, the stochastic nature of real-life solar irradiance has been a major issue for solar photovoltaic (PV) system planning and performance evaluation. This paper aims to discover clearness index (CI) patterns and to construct centroids for the daily CI profiles. This will be useful in being able to provide a standardized methodology for PV system design and analysis. Four years of solar irradiance data collected from Johannesburg (26.21S, 28.05E), South Africa are used for the case study. The variation in CI could be significant in different seasons. In this paper, cluster analysis with Gaussian Mixture Models, K-Means with Euclidean distance, K-Means with Manhattan distance, Fuzzy C-Means with Euclidean distance and Fuzzy C-Means with Dynamic Time Warping (FCM DTW) are performed for the four seasons. A case study based on sizing a stand-alone solar PV and storage system with anaerobic digestion biogas power plants is used to examine the usefulness of the clustering results. It concludes that FCM DTW and GMM can determine the correct PV farm rated capacity with an acceptable energy storage capacity, with 36 and 46 rather than 1457 solar irradiance profiles respectively.

Control Strategy of a Hybrid Energy Storage System to Smooth Photovoltaic Power Fluctuations Considering Photovoltaic Output Power Curtailment

“…Based on the database that recorded the active power data of a 750 kW PV plant for one year, these daily active power curves of PV can be divided into three main categories using the k-means clustering algorithm [23]: (1) Scenario I; (2) scenario II; and (3) scenario III, as shown in Figure 6. Due to the limited size of the figure, only three daily power curves were plotted in each scenario.…”

“…As can be seen from Figure 17, the simulation results demonstrate that the proposed smoothing strategy is more economical than the reference strategy. (23) where EPG denotes the annual power generation of the PV plant. Referring to the report released by the US Energy Information Administration, the feed-in price of solar power, m1, is set to US$0.13/kWh [28].…”

A robust correlation analysis framework for imbalanced and dichotomous data with uncertainty

“…Using CI in Equation 19is one method to evaluate the influence of weather conditions with respect to solar irradiance (Lai et al, 2017a). The analysis of these fluctuations with regard to solar energy applications should focus on the instantaneous CI (Kheradmanda et al, 2016;Liu et al, 2015a;Woyte et al, 2007;Woyte et al, 2006).…”

“…CI can effectively characterize the attenuating impact of the atmosphere on solar irradiance by specifying the proportion of extraterrestrial solar radiation that reaches the surface of the earth. In Equation (19) for each time of the year, is the irradiance on the surface of the earth measured with a pyranometer device and is the clear-sky solar irradiance (Lai et al, 2017a). The CI value will be between 0 and 1, where 0 and 1 indicate no solar irradiance and the maximum amount of solar irradiance will arrive on the surface of earth, respectively.…”

Robust Optimization for Hydroelectric System Operation Under Uncertainty

“…The computation of good features is highly context-dependent, and relies on the knowledge the user has of the data at hand. In this work, clearness indices (CI), defined as the ratio between the real observed solar production and the ideal solar production obtained using a clear-sky deterministic atmospheric model [31], are used as features. A clustering on raw data is also performed for comparison.…”