Solar Photovoltaic Power Generation Regional Agent

What are the regional competition patterns in photovoltaic power installation?

Regional competition patterns Through the spatial autocorrelation analysis by stage, the global Moran indexes can be obtained as 0.1027, 0.2237, 0.1131, 0.1747, −0.1577 and 0.1050, indicating that the layout of photovoltaic power installation is not randomly distributed in each province, but the certain spatial correlation characteristics exist.

Is solar PV a strategic renewable technology?

This report clearly points out that solar PV is one of the strategic renewable technologies needed to realise the global energy transformation in line with the Paris climate goals. The technology is available now, could be deployed quickly at a large scale and is cost-competitive.

Is solar PV a competitive source of new power generation capacity?

Solar PV is emerging as one of the most competitive sources of new power generation capacity after a decade of dramatic cost declines. A decline of 74% in total installed costs was observed between 2010 and 2018 (Figure 10).

What is the regional distribution of photovoltaic power stations in China?

In general, the regional distribution of photovoltaic power stations in China is quite different, and the regional competition patterns are variable. Provinces with high installed photovoltaic power stations and high regional competition are mainly located in Northwest and North China.

What are the features of a regional photovoltaic power cluster?

These features include wind speed (WS), wind direction (WD), temperature (T), pressure (P), and humidity (H). For wind direction, we use sin function to convert the angle value. Similar to regional wind power cluster, the regional photovoltaic power cluster also contains three photovoltaic power stations.

Does a high-resolution global assessment of rooftop solar photovoltaics potential exist?

Yet, only limited information is available on its global potential and associated costs at a high spatiotemporal resolution. Here, we present a high-resolution global assessment of rooftop solar photovoltaics potential using big data, machine learning and geospatial analysis.