The research plan comprises:
Task 1 - Spatiotemporal modeling of relevant climate variables (12M). Statistical-based models of the spatial-temporal distribution of climate variables (e.g., solar irradiance, wind speed, etc.) will be generated for the next 30 years using legacy data available at IPMA and from LandSaF satellite. The spatial description will be modeled through geostatistical simulation and co-simulation methods, integrating data with distinct quality, and assessing the uncertainty about predictions. The spatial models will be evolved in time with the application of innovative geo-spatial data science tools (e.g., deep neural networks; DNN) to model the temporal evolution of the system.
Task 2 - Forecasting of renewable energy power generation and load demand (8M). Temporal WT/PV power density maps will be built from climate models (T1). A hybrid mechanistic-ML (e.g., DNN) modeling approach will be developed by combining operating power curves of WTs and PV modules with historical data available at REN, the location and characteristics of installed WT/PV farms in Portugal. Load demand time series will be gathered from legacy data and linked with calendar, climate, and social-economic data. Stochastic optimization algorithms (e.g., particle swarm techniques) will be considered to train the models.
Task 3 - Evaluation of electrolyzer plants in terms of economic performance and dynamic response (16M). ALKEL/PEMEL plants (electrolysis cells, gas separation, H2 purification) will be modeled and designed in detail using specialized software. Heat/power integration methods and flowsheet optimization will be applied to reduce levelized cost of energy (LCOE) and enhance energy efficiency for various H2 production rates. Optimal sizing of WT/PV/electrolyzer/H2 storage systems satisfying the forecasted load demand within the timeframe will be conducted. These systems’ dynamic response will be analyzed, and mechanistic-ML hybrid control schemes developed.