Annular Directed Distributed Algorithm for Energy Internet

Table 4

Application scenario analysis.

Serial number	Scene	Regional characteristics	Typical case	Fit for traditional distributed methods	Fit for the proposed distributed method
A1	Suburb type	Small area with low energy demand	Urban	✖	✔
A2	Seasonal switching type	Need heat in winter and cold in summer. The demand of power and gas is large.	Cities in Yangtze plain, middle and lower, China	✔	✖
A3	Cold plateau	The atmospheric pressure is low because of high altitude, and the temperature is cold for the same reason. All energy demands are low.	Qinghai-Tibet plateau, China	✖	✖
A4	Island (in sea)	Wind and petroleum are rich	Nansha six reefs, China	✖	✔
A5	Cold area	Heat load is high	Northeast, China	✔	✖
A6	Mountainous	Energy load is low. Wind and solar are rich.	Southwest, China	✖	✔
A7	High latitude port	DC load of ships is high. Heat load is high because the temperature is cold	Port cities in Northern Europe and North America	✖	✔
A8	Dispersing area	The natural gas is rich, and the energy load is dispersed	Northwest, China	✖	✔