Abstract
In recent years, the penetration of photovoltaic (PV) power generation in Taiwan has increased significantly. However, most photovoltaic facilities, especially for small-scale sites, do not include relevant monitoring and real-time measurement devices. The invisible power generation from these PV sites would cause a huge challenge on power system scheduling. Therefore, appropriate methods to estimate invisible PV power generation are needed. The main purpose of this paper is to propose an improved fuzzy model for estimating the PV power generation, which includes the clustering processing for PV sites, selection of representative PV sites, and the improvement of the conventional fuzzy model. First, this research uses the K-nearest neighbor (KNN) algorithm to fill in some of the missing data; then, two clustering algorithms are applied to cluster all the photovoltaic sites. Next, the relationship between the power generation of a single PV site and the total generation of all sites at the same cluster is further analyzed to select the representative PV sites. Finally, an improved fuzzy model is implemented to estimate the PV power generation. This research used actual data that were measured from PV sites in Taiwan for the estimation, verification, and comparison study. The numerical results demonstrate that the proposed method can obtain an average estimation error about 7% by using limit measurements from PV sites, highlighting the high efficiency and practicability of the proposed method.
Highlights
Taiwan will significantly boost renewable energy generation in the future, and its target is 20 GW installed capacity of solar power generation by 2025
The geographical location of each PV plant is illustrated in Figure 5; this figure includes the map of Taiwan which reveals the distribution of all the PV sites with latitude and longitude coordinates; each cross symbol indicates a PV site
It is hard to show the detailed location of each PV site from Figure 5, but it can be observed that most PV sites are located at central and southern Taiwan
Summary
Taiwan will significantly boost renewable energy generation in the future, and its target is 20 GW installed capacity of solar power generation by 2025. Many solar power systems lack the installation of monitoring instruments, system operators are unable to determine the actual amount of electricity the is produced, posing numerous challenges in system scheduling and monitoring. As these “invisible” or behindthe-meter (BTM) PV sites increases, they can directly reshape the net load curve of the system. Several studies [2,3,4,5] have developed model-based approaches for estimating PV power generation; those approaches considered diverse meteorological data and physical PV models They would be considerably hampered by the inaccurate PV geometry data, as well as the lack of system parameters. While supervised or semi-supervised methods necessitate all or a subset of historical PV power generation and load data from load customers [6,7,8,9,10,11], unsupervised approaches are based primarily on real-time power measurements [12,13,14]
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