DocumentCode :
1778925
Title :
Generation expansion planning considering externalities for large scale integration of renewable energy
Author :
Khan, A. Zeb ; Sun Yingyun ; Ashfaq, Ahsan
Author_Institution :
State Key Lab. of Alternate Electr. Power Syst., North China Electr. Power Univ., Beijing, China
fYear :
2014
fDate :
2-6 June 2014
Firstpage :
135
Lastpage :
140
Abstract :
Generation expansion planning (GEP) problem involves determining new power plants in terms of type, capacity and allocation for meeting future load demands. The GEP problem incorporates large number of constraints and it is a highly complex, large scale, non linear optimization problem. Large scale integration of Renewable Energy systems (RES) in GEP problem has always been averted by the high cost and poor availability rates of RES as compared to conventional fossil fuel fired power plants. However internalizing environmental externalities in GEP problem could help to promote large scale integration of RES. The proposed model in this paper examines the effect of internalizing externalities on the GEP problem. External cost values for China are evaluated using the unit damage costs of pollutants. The model has been implemented in MATLAB using Genetic Algorithm and is applied to a case study of one of the district power companies of China. The results show significant structural changes in favor of RES confirming that internalizing externalities can be used as a driving policy tool for large scale integration of RES.
Keywords :
genetic algorithms; power generation planning; renewable energy sources; China; GEP problem; MATLAB; RES; district power companies; external cost values; externalities internalization; future load demands; generation expansion planning problem; genetic algorithm; large scale integration; new power plants; nonlinear optimization problem; renewable energy systems; unit damage costs; Coal; Electricity; Fossil fuels; Genetic algorithms; Planning; Power generation; Power systems; External Cost of Energy; Generation Expansion Planning (GEP); Genetic Algorithm(GA); Greenhouse Gas(GHG); Renewable Energy Systems(RES);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Energy and Power Systems (IEPS), 2014 IEEE International Conference on
Conference_Location :
Kyiv
Print_ISBN :
978-1-4799-2265-9
Type :
conf
DOI :
10.1109/IEPS.2014.6874165
Filename :
6874165
Link To Document :
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