In Compressed Air Energy Storage (CAES), energy is stored in the form of compressed air in a reservoir during off-peak periods, while it is used on demand during peak periods to generate power with a turbo-generator system. Such plants can offer significant benefits in terms of flexibility in matching a fluctuating power demand, particularly when coupled with renewable sources, characterized by high and often unpredictable variability. A model of a hybrid power plant consisting of CAES coupled with a wind farm is presented. The model employs neural network-based wind speed forecasting. By storing surplus energy some of the major limitations of wind source (low power density, unpredictable nature, etc) can be overcome. The use of time-series neural network-based prediction models aims at reducing the stochastic uncertainty of wind power and can be a powerful means for planning the daily operation strategy of the storage system. Based on CAES technical data of the Alabama plant and wind turbine data provided by GE, a detailed economic analysis has been carried out: investment and maintenance costs are estimated based on literature data, while operational costs are calculated according the actual energy market prices. The results show advantages in terms of Net Present Value, energy savings and CO2 emissions.

Use of Wind Forecast for the Management of a Hybrid Power Plant with Wind Turbines and Compressed Air Energy Storage

ARSIE, Ivan;MARANO, VINCENZO;RIZZO, Gianfranco;
2006-01-01

Abstract

In Compressed Air Energy Storage (CAES), energy is stored in the form of compressed air in a reservoir during off-peak periods, while it is used on demand during peak periods to generate power with a turbo-generator system. Such plants can offer significant benefits in terms of flexibility in matching a fluctuating power demand, particularly when coupled with renewable sources, characterized by high and often unpredictable variability. A model of a hybrid power plant consisting of CAES coupled with a wind farm is presented. The model employs neural network-based wind speed forecasting. By storing surplus energy some of the major limitations of wind source (low power density, unpredictable nature, etc) can be overcome. The use of time-series neural network-based prediction models aims at reducing the stochastic uncertainty of wind power and can be a powerful means for planning the daily operation strategy of the storage system. Based on CAES technical data of the Alabama plant and wind turbine data provided by GE, a detailed economic analysis has been carried out: investment and maintenance costs are estimated based on literature data, while operational costs are calculated according the actual energy market prices. The results show advantages in terms of Net Present Value, energy savings and CO2 emissions.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/3881663
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