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Distributed photovoltaics (PV) play a key role in the energy transition. We construct an investment value and timing assessment model based on the real options method to improve scientific decision-making in distributed PV investing in an uncertain environment. The model is applied to analyze projects in the East China Power Grid region under multiple self-consumption ratio scenarios. The results show that investing in distributed PV projects is beneficial, but there are differences in the optimal investment timing under different self-consumption ratios. Investors can defer investment for as early as one year to obtain optimal investment value. • Propose the distributed PV investment decision model based on the real option method. • Consider the uncertainty of initial investment cost and the on-grid price. • Investigate the impact of distributed PV power generation self-consumption ratio. • Investigate the impact of carbon trading on investment value and investment timing. [ABSTRACT FROM AUTHOR]

With the increasing penetration of information and communication technologies (ICTs) in energy systems, traditional energy systems are being digitized. Advanced analysis of the energy production and consumption data and data-driven decision support can be combined to promote the formation and development of smart energy systems. Smart grids are a specific application of smart energy systems. Different electricity consumption patterns of residential users can be discovered and extracted by clustering analysis of the electricity consumption data collected by smart meters and other data acquisition terminals in a smart grid. This research explores daily electricity consumption patterns of low-voltage residential users in China. The service architecture of smart power use and the structure of electric energy data acquisition system of the State Grid Corporation of China (SGCC) are introduced and a process model for mining daily electricity consumption data is presented. The analysis is based on the fuzzy c-means (FCM) clustering method and a fuzzy cluster validity index (PBMF). A case study of Kunshan City, Jiangsu Province, China is presented, using the daily electricity consumption data of 1312 low-voltage users within a month. [ABSTRACT FROM AUTHOR]