DocumentCode :
185061
Title :
Revenue maximization of electricity generation for a wind turbine integrated with a Compressed Air Energy Storage system
Author :
Saadat, Mahmoud ; Shirazi, Farzad A. ; Li, Perry Y.
Author_Institution :
Mech. Eng. Dept., Univ. of Minnesota, Minneapolis, MN, USA
fYear :
2014
fDate :
4-6 June 2014
Firstpage :
1560
Lastpage :
1565
Abstract :
A high-level supervisory controller is developed for a Compressed Air Energy Storage (CAES) system integrated with a wind turbine. Complementary to the low-level controllers in our previous works to track generator power and pressure, this controller coordinates different subsystems to optimize the system performance. The control strategy is obtained by solving an optimal storage/regeneration problem in order to maximize the achievable total revenue from selling electricity to the electric grid. Dynamic Programming (DP) approach is used to solve the corresponding optimal control problem that accounts for all the major losses in the CAES system as well as its nonlinear dynamics. Results show that an increase of 51% in total revenue is achievable by using the CAES system for a conventional wind turbine. Furthermore, a case study has been conducted to investigate the effect of storage system sizing on the maximum revenue.
Keywords :
compressed air energy storage; dynamic programming; power generation control; power generation economics; power grids; wind turbines; CAES system; DP approach; compressed air energy storage system; control strategy; dynamic programming approach; electric grid; electricity generation revenue maximization; electricity selling; high-level supervisory controller; optimal regeneration problem; optimal storage problem; power generation tracking; wind turbine integration; Electricity; Energy storage; Generators; Liquids; Optimal control; Wind power generation; Wind turbines; Fluid power control; Optimal control; Optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
American Control Conference (ACC), 2014
Conference_Location :
Portland, OR
ISSN :
0743-1619
Print_ISBN :
978-1-4799-3272-6
Type :
conf
DOI :
10.1109/ACC.2014.6859445
Filename :
6859445
Link To Document :
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