DocumentCode :
238957
Title :
Optimal sizing of DGs and storage for microgrid with interruptible load using improved NSGA-II
Author :
Zhe Shi ; Yonggang Peng ; Wei Wei
Author_Institution :
Coll. of Electr. Eng., Zhejiang Univ., Hangzhou, China
fYear :
2014
fDate :
6-11 July 2014
Firstpage :
2108
Lastpage :
2115
Abstract :
The rapid development of distributed generation (DG) has deeply transferred the power utilization style. Microgrid is developed for better absorption of distributed generation and has been researched in recent years. Interruptible load (IL) is another method to absorb the randomness and waviness of wind and solar energy, and is considered in this paper for more reliable and efficient deployment of DGs and storage in microgrid. A multi-objective optimization model is proposed for microgrid power sources construction with distributed generation, storage and interruptible load. Objectives of the model are economic cost, environmental cost and annual interruption duration. The model is solved by employing improved NSGA-II with the input of temperature, light intensity, wind speed, and load curve. The case study shows that the Pareto optimal front which covers the optimal solutions under different circumstances is effectively obtained. Thus the supervisor can select the final scheme with full consideration of different objectives. The impacts of IL on economic and environmental cost are also analyzed and demonstrated with many aspects.
Keywords :
Pareto optimisation; distributed power generation; genetic algorithms; power generation economics; DG optimal sizing; NSGA-II; Pareto optimal front; annual interruption duration; distributed generation; economic cost; energy storage; environmental cost; interruptible load; light intensity; load curve; microgrid power sources; multiobjective optimization model; wind speed; Batteries; Load modeling; Microgrids; Optimization; Sociology; Statistics; Wind turbines; distributed generation; improved NSGA-II; interruptible load; microgrid;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation (CEC), 2014 IEEE Congress on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-6626-4
Type :
conf
DOI :
10.1109/CEC.2014.6900414
Filename :
6900414
Link To Document :
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