After a general overview of Hybrid Power Plants (HPP) and Compressed Air Energy Storage (CAES), the authors present a thermo-economic model for the simulation and optimization of a HPP consisting of a wind turbine coupled with CAES. In the proposed scheme, during periods of excess power production, atmospheric air is compressed in a multistage compressor and cooled; when there is power demand, the compressed air is heated in multiple expansion stages using the stored heat and conventional thermal sources. Such plants can offer significant benefits in terms of flexibility in matching a fluctuating power demand, particularly when renewable sources, characterized by high and often unpredictable variability, are utilized. The possible advantages in terms of energy and cost savings with respect to other solutions must be carefully assessed, critically depending on performance and efficiencies of each sub-system, most of them operating in transient and off-design conditions. To this purpose, a thermodynamic model composed of several sub-systems describing wind turbine, multi-stage compressor, intercooler, aftercooler, heat recovery system, compressed air storage and turbine has been developed in Matlab/Simulink® environment. In the paper, several scenarios are compared by simulation and optimization analysis and a parametric study of the plant performance with respect to the main design variables is presented
This article presents the development of a constrained optimization algorithm, whose scope is to support the preliminary design of a renewable microgrid, integrating solar panels and wind turbines with reversible solid oxide cells. The motivations behind this research activity lie in the increasing interest in renewable-based production and on-site storage of hydrogen, and its aim is to help this energy vector spread worldwide and in as many industrial and residential sectors as possible within a reasonably short timeframe. To this end, suitable models were developed by referring to the most relevant literature and by introducing some specific simplifying assumptions. Such an approach allowed the setting-up of a multi-variable constrained optimization task, whose outcomes correspond to the most techno-economic effective plant configuration with respect to assigned design criteria. The optimum solution was particularly sought via the generalized reduced gradient method, aimed at determining renewable plants sizes under the constraint that the final stored hydrogen level is brought back to the initial value after one year. The results highlight that an interesting payback time of about 10 years can be attained, while guaranteeing that the optimal configuration holds promising resiliency and islanded-use capabilities (such as almost weekly self-sufficiency) via smart over-the-year charge-sustaining management of the designed hydrogen storage tank. In this way, it was possible to simultaneously address, via the specific optimization problem formulation, the interconnected needs of optimally designing system components in terms of installed power, and the proper management of the reversible solid oxide cell unit.
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