Daily data for energy management: Renewable generation, consumption and storage

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.