Abstract
Due to the significant variations in electricity generation and its demand, the power plant owners are encountered with challenges of economic operation. Among all, Compressed air energy storage (CAES) technology has proposed itself as a reliable and efficient solution to match the two sides. This paper deals with a modeled compressed air energy storage power plant which has been optimized thermodynamically through an efficient genetic algorithm code. The results of this optimized model, considered as the base case, show that the power plant is technically and financially justifiable. In order to obtain a more tangible realization, it is necessary to verify the results against the variation of key parameters. In this study, the sensitivity analysis is performed based on main parameters including plant loading and ambient condition and the resultant trends of each case are presented. This approach will help the designers to analyze the quality of their designs in different situations.
Highlights
Electrical Energy Storage (EES) refers to a process of converting electrical energy from a power network into a form that can be converted back to electrical energy when needed [1]
This paper aims at different conditions at which a Compressed air energy storage (CAES) may be operated
5.4 variable area nozzle (VAN) and IGV effect One of the key parameters which plays a prominent role in compressor stability especially during the start up and shut down period is the percentage of inlet guide van (IGV) opening
Summary
Electrical Energy Storage (EES) refers to a process of converting electrical energy from a power network into a form that can be converted back to electrical energy when needed [1] Such a process enables electricity to be produced at times of low demand, low generation cost or from intermittent energy sources and to be used at times of high demand, high generation cost or when no other generation means is available. The effect of off-design condition parameters on the plant performance, which would help the plant designers and operators to make the best decisions, has been studied
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