Application of dynamic programming to the optimal management of a hybrid power plant with wind turbines, photovoltaic panels and compressed air energy storage

A model for thermo-economic analysis and optimization of a hybrid power plant consisting of compressed air energy storage (CAES) coupled with a wind farm and a photovoltaic plant is presented. This kind of plant is aiming to overcome some of the major limitations of renewable energy sources, represented by their low power density and intermittent nature, largely depending upon local site and unpredictable weather conditions. S, 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. ematical model, validated in a previous study over the CAES plant in Alabama, US, is coupled with a dynamic programming algorithm to achieve the optimal management of the plant, in order to minimize operational costs while satisfying constraints related to the operation of reservoir, compressors and turbines, also considering their off-design performance. The potential benefits of such plant in terms of energy consumption and CO2 emission are analyzed and discussed, for different configurations and scenarios.