Fang, H.; Shang, L.; Dong, X.; Tian, Y. High Proportion of Distributed PV Reliability Planning Method Based on Big Data. Energies2023, 16, 7692.
Fang, H.; Shang, L.; Dong, X.; Tian, Y. High Proportion of Distributed PV Reliability Planning Method Based on Big Data. Energies 2023, 16, 7692.
Fang, H.; Shang, L.; Dong, X.; Tian, Y. High Proportion of Distributed PV Reliability Planning Method Based on Big Data. Energies2023, 16, 7692.
Fang, H.; Shang, L.; Dong, X.; Tian, Y. High Proportion of Distributed PV Reliability Planning Method Based on Big Data. Energies 2023, 16, 7692.
Abstract
With a high proportion of distributed photovoltaic and lower fossil energy integrated into the distribution network, it is very difficult to ensure the reliability of power supply. The distributed photovoltaic planning model based on big data is proposed. According to the impact stochastic photovoltaics and loads on reliability planning, the static and dynamic capacity-load ratios are proposed. The big data analysis model of distributed photovoltaic planning is established. The big data multi-scenario generation and reduction algorithm of stochastic distributed photovoltaic and load planning is studied, and a source-load big data scenario matching model is proposed. Ac-cording to the source load big data scenario, the dynamic capacity-load ratio of the distribution network is obtained. The static capacity-load ratio calculation method in distribution network planning is studied to ensure the reliability of power supply. Finally, the IEEE 33-bus system is used as an example. The results show that distributed photovoltaic planning based on big data and multi-scenario methods can improve photovoltaic utilization and power supply reliability.
Keywords
distributed photovoltaic; big data; planning; reliability; multi-scenario
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
