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Updated: Sep 19, 2025

Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data
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Daily data for energy management: Renewable generation, consumption and storage.

Tayenne Dias de Lima1, Bruno Ribeiro1, Pedro Faria1

  • 1Intelligent Systems Associate Laboratory (LASI), GECAD, ISEP - Polytechnic of Porto, Porto, 4200-072, Portugal.

Data in Brief
|June 19, 2025
PubMed
Summary

This dataset offers crucial data for power system planning, including renewable energy scenarios and real battery usage profiles. It aids in managing energy uncertainties and optimizing battery storage systems for better grid operations.

Keywords:
Battery charge/discharge profilesDynamic time-warpingRepresentative scenariosSolar generationUncertaintiesk-Medoid

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Area of Science:

  • Power Systems Engineering
  • Renewable Energy Integration
  • Data Science for Energy

Background:

  • Renewable energy variability and demand uncertainty pose significant challenges for power system planning and operation.
  • Stochastic programming models require representative data to effectively incorporate uncertainty.
  • Real-world battery charge/discharge data is vital for developing energy storage solutions.

Purpose of the Study:

  • To provide a comprehensive dataset addressing uncertainties in renewable energy generation and demand.
  • To offer real-world battery operational data for energy storage research.
  • To support the development of robust power system planning and operational strategies.

Main Methods:

  • Generation of representative scenarios for solar power, energy prices, and demand using k-medoid clustering and dynamic time warping (DTW).
  • Collection and analysis of real battery charge and discharge data from operational use.
  • Integration of temporal correlations within seasonal data blocks (winter, spring, summer, autumn).

Main Results:

  • A dataset featuring hourly solar power output, energy prices, and demand scenarios capturing historical variability.
  • A dataset detailing real battery charge/discharge patterns, durations, and depths.
  • Demonstration of data applicability for scenario-based stochastic programming and battery storage system research.

Conclusions:

  • The dataset provides a valuable resource for enhancing power system planning and operational decision-making.
  • It facilitates the integration of renewable energy sources by addressing variability and uncertainty.
  • It supports advancements in battery storage system research and grid integration.