Bridging the Data Gap: Integrating Climate Projections into Grid Planning for Weather-Driven Events

The impact of changing climate conditions on power systems is increasingly evident, with rising risks of thermal outages and variability in renewable generation patterns. High fidelity weather and climate data are crucial but often overlooked in grid planning models that inform long-term decision-making. This paper presents a streamlined, computationally efficient framework that leverages advanced machine learning techniques to incorporate high-quality climate data into power system models, effectively bridging the data gap. The proposed three-stage architecture selects representative regions and periods, and identifies periods of extreme weather events based on relevant weather variables, such as temperature, wind speed, solar irradiance, which are inputs to estimate corresponding grid inputs, e.g. load, power generation profiles and outage probabilities. By enhancing the fidelity of climate data integration, this framework enables a robust approach to capture climate-induced impacts, thereby strengthening long-term planning in power systems.

• Publication year

  2025

• Venue

  IEEE Power & Energy Society General Meeting

• Publication date

  2025-07-27

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/PESGM52009.2025.11225462
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• On representative day selection for capacity expansion planning of power systems under extreme operating conditions

  2021cited by this paper
• Revisiting Time Series Outlier Detection: Definitions and Benchmarks

  2021cited by this paper
• Extreme events in time series aggregation: A case study for optimal residential energy supply systems

  2020cited by this paper
• Improvement of representative days selection in power system planning by incorporating the extreme days of the net load to take account of the variability and intermittency of renewable resources

  2020cited by this paper
• Representative Days for Expansion Decisions in Power Systems

  2019cited by this paper
• k-MILP: A novel clustering approach to select typical and extreme days for multi-energy systems design optimization

  2019cited by this paper
• How does climate change affect electricity system planning and optimal allocation of variable renewable energy?

  2019cited by this paper
• Clustering representative days for power systems generation expansion planning: Capturing the effects of variable renewables and energy storage

  2019cited by this paper
• Evaluations of high-resolution dynamically downscaled ensembles over the contiguous United States

  2018cited by this paper
• Dealing with multiple decades of hourly wind and PV time series in energy models: A comparison of methods to reduce time resolution and the planning implications of inter-annual variability

  2017cited by this paper
• Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data

  2017cited by this paper
• Impact of different time series aggregation methods on optimal energy system design

  2017cited by this paper
• Clustering algorithms for the selection of typical demand days for the optimal design of building energy systems

  2016cited by this paper

• No citing papers are available for this paper.