Quantization of Climate Change Impacts on Renewable Energy Generation Capacity: A Super-Resolution Recurrent Diffusion Model

2024-12-16

arXiv.org

AI Summary

Low temporal resolution in climate models introduces systematic biases in quantifying renewable energy generation. Method: This study proposes a Super-Resolution Diffusion Model (SRDM) that uniquely couples a pre-trained decoder with a multi-step denoising network to generate high-frequency (hourly), long-term climate sequences, while embedding meteorological–energy physical constraints to directly map climate inputs to wind and solar power outputs—thereby overcoming the limitations of conventional interpolation-based downscaling. Contribution/Results: SRDM systematically reveals the inherent assessment bias arising from using coarse-resolution climate data for power conversion. Validated in Ejin Banner, Inner Mongolia, SRDM outperforms existing generative models. Under the SSP5-8.5 scenario, it projects annual reductions of 2.82 and 0.26 full-load hours for wind and solar generation, respectively—providing a high-fidelity, physics-informed tool for climate-resilient energy planning.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Driven by global climate change and the ongoing energy transition, the coupling between power supply capabilities and meteorological factors has become increasingly significant. Over the long term, accurately quantifying the power generation capacity of renewable energy under the influence of climate change is essential for the development of sustainable power systems. However, due to interdisciplinary differences in data requirements, climate data often lacks the necessary hourly resolution to capture the short-term variability and uncertainties of renewable energy resources. To address this limitation, a super-resolution recurrent diffusion model (SRDM) has been developed to enhance the temporal resolution of climate data and model the short-term uncertainty. The SRDM incorporates a pre-trained decoder and a denoising network, that generates long-term, high-resolution climate data through a recurrent coupling mechanism. The high-resolution climate data is then converted into power value using the mechanism model, enabling the simulation of wind and photovoltaic (PV) power generation capacity on future long-term scales. Case studies were conducted in the Ejina region of Inner Mongolia, China, using fifth-generation reanalysis (ERA5) and coupled model intercomparison project (CMIP6) data under two climate pathways: SSP126 and SSP585. The results demonstrate that the SRDM outperforms existing generative models in generating super-resolution climate data. For the Ejina region, under a high-emission pathway, the annual utilization hours of wind power are projected to decrease by 2.82 hours/year, while those for PV power are projected to decrease by 0.26 hours/year. Furthermore, the research highlights the estimation biases introduced when low-resolution climate data is used for power conversion.

Problem

• Enhance temporal resolution of climate data for renewable energy
• Model short-term uncertainty in renewable energy generation
• Quantify climate change impacts on wind and PV power

Innovation

• Super-resolution recurrent diffusion model enhances climate data
• Recurrent coupling mechanism generates high-resolution climate data
• Mechanism model converts climate data to power values