Daily data for energy management: Renewable generation, consumption and storageZenodo - Tayenne Dias de Lima; Bruno Ribeiro; Pedro Faria; Luis Gomes; Zita Vale (Data in Brief)
The variability and uncertainty of renewable energy generation and demand present significant challenges for the planning and operation of power systems. Developing representative data to address these uncertainties is common in stochastic programming models, which employ scenario-based approaches to incorporate uncertainty into decision-making processes. Furthermore, access to data on battery charge and discharge profiles in real applications is essential to develop effective energy storage and management solutions. Thus, this dataset provides two distinct yet complementary components. The first component includes hourly data over a year for solar power output, energy prices, and demand, categorized into seasonal blocks: winter, spring, summer, and autumn. These datasets preserve temporal correlations and are processed using k-medoid clustering and the dynamic time-warping (DTW) distance metric to generate representative scenarios. These representative scenarios capture the variability and key characteristics of historical data. This data can be used in scenario-based stochastic programming models. The second component comprises real battery charge and discharge collected at the GECAD Research Center. These data provide insights into battery behaviour under operational conditions, including charge/discharge patterns, durations, and depths. This data is particularly useful for researching battery storage systems and their integration into power systems. Finally, these components serve as a valuable resource for addressing power system challenges and can be effectively applied to operational and planning problems