Dr. Vincenzo Marano is a Senior Research Associate at The Ohio State University Center for Automotive Research (CAR). He received his B.S./M.S. "cum laude" in Mechanical Engineering in 2003 and his Ph.D. in Mechanical Engineering in 2007, all from the University of Salerno (Italy). He started his collaboration with CAR in 2005 while working on his PhD and then was appointed as Postdoctoral Research Fellow in 2007. His research interests are in the areas of energy systems and alternative vehicles. Dr. Marano coordinates and conducts research on plug-in hybrid electric vehicles, energy storage, energy management, control strategies for PHEVs, their interaction with renewable energy sources and the grid, macroeconomics and energy policy. Dr. Marano is currently involved with Oak Ridge National Laboratory (ORNL), General Electric (GE), Electric Power Research Institute (EPRI), in a Department of Energy (DOE) program aiming at studying benefits, barriers, opportunities, and challenges of grid-connected, plug-in hybrid vehicles (PHEV) in order to establish potential value propositions that will lead to commercially viable PHEVs. Since 2008, Dr. Marano has served as program manager of the SMART@CAR consortium, a collaborative research program of The Ohio State University - Center for Automotive Research with participation of major automotive OEMs and electric power companies.
Abstract: Some of the major limitations of renewable energy sources are represented by their low power density and intermittent nature, largely depending upon local site and unpredictable weather conditions. These problems concur to increase the unit costs of wind power, so limiting their diffusion. By coupling storage systems with a wind farm, some of the major limitations of wind power, such as a low power density and an unpredictable nature, can be overcome. Furthermore, the use of time-series neural network-based prediction models aims at reducing the stochastic uncertainty of wind power. A Matlab/Simulink model of a hybrid power plant consisting of a wind farm coupled with Compressed Air Energy Storage (CAES) is presented. In CAES energy is stored as 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. The model employs ANN-based wind speed forecasting to determine the optimal daily operation strategy for the storage system. As shown in the paper, the knowledge of the expected available energy is a key factor to optimize the management strategies of the proposed hybrid power plant. A detailed economic analysis has been carried out: investment and maintenance costs are estimated based on literature data, while operational costs and revenues are calculated according to the Italian energy market prices.
Abstract: 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 multi-stage 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.
