Lucio Ippolito

Clean hydrogen (H₂) use (i.e., produced using either renewable or low-carbon energy sources) can help decarbonize energy-intensive industries, the transport sector, and the power sector. The European regulatory framework establishes that the production of green H₂ must be supported either by the electricity grid through a power purchase agreement (PPA) or by intermittent renewable energy source (RES) plants owned by the hydrogen producer. Although the issue of the optimization of hydrogen production costs has already been approached, constraints related to the current regulatory framework and the modeling of nonlinear electrolyzer efficiency still represent open problems. In this paper, a mixed-integer linear programming (MILP) problem, assuming as the objective function the overall cost minimization of the allowed energy mix for green H₂ production, is formulated. Two approaches are compared: in the first one, electrolyzers can only operate at 100% load, whereas the second one allows for more flexible electrolyzer scheduling, by enabling partial-load working operations. The simulation results of several scenarios considering different H₂ production targets, forecasted RES production, and cost for PPAs demonstrate the effectiveness of the proposed methodology.

This study introduces a novel hybrid forecasting model for wind power generation. It integrates Artificial Neural Networks, data clustering, and Particle Swarm Optimization algorithms. The methodology employs a systematic framework: initial clustering of weather data via the k-means algorithm, followed by Pearson’s analysis to pinpoint pivotal elements in each cluster. Subsequently, a Multi-Layer Perceptron Artificial Neural Network undergoes training with a Particle Swarm Optimization algorithm, enhancing convergence and minimizing prediction discrepancies. An important focus of this study is to streamline wind forecasting. By judiciously utilizing only sixteen observation points near a wind farm plant, in contrast to the complex global numerical weather prediction systems employed by the European Center Medium Weather Forecast, which rely on thousands of data points, this approach not only enhances forecast accuracy but also significantly simplifies the modeling process. Validation is performed using data from the Italian National Meteorological Centre. Comparative assessments against both a persistence model and actual wind farm data from Southern Italy substantiate the superior performance of the proposed hybrid model. Specifically, the clustered Particle Swarm Optimization-Artificial Neural Network-Wind Forecasting Method demonstrates a noteworthy improvement, with a reduction in mean absolute percentage error of up to 59.47% and a decrease in root mean square error of up to 52.27% when compared to the persistence model.