Title :
Study on methodology of generation expansion planning for power restructuring
Author_Institution :
State Power Economic Res. Center of China, Beijing, China
Abstract :
China is in the process of restructuring the power sector. Power planning will play an important role in the successful power reform. However, restructuring will substantially change power planning, especially the methodology of the power generation expansion planning. The used methodologies such as integrated resources planning (IRP) and other generation expansion planning cannot meet the situation of power restructuring. In this paper, it is proposed to study a methodology of power generation expansion planning by using intelligent engineering (artificial intelligence, neural networks and fuzzy logic) to conduct scenario studies and to assess the impact of various institutional, economical, political and technological strategies in sustainable electrification. By using intelligent engineering as an underlying component it provides alternative pathways to the planning with a smart solution. The methodology is aimed at contributing to a broader understanding of the planning for both government in national level and power supply companies in regional level.
Keywords :
electricity supply industry deregulation; fuzzy set theory; neural nets; power engineering computing; power generation planning; China; artificial intelligence; economical strategies; electricity demand forecasting; fuzzy logic; fuzzy sets; generation expansion planning; institutional strategies; intelligent engineering; neural networks; political strategies; power restructuring; sustainable electrification; technological strategies; Artificial intelligence; Artificial neural networks; Intelligent networks; Meeting planning; Power engineering and energy; Power generation; Power generation economics; Power generation planning; Strategic planning; Technology planning;
Conference_Titel :
Power System Technology, 2002. Proceedings. PowerCon 2002. International Conference on
Print_ISBN :
0-7803-7459-2
DOI :
10.1109/ICPST.2002.1053572