Incorporating climate change effects into the European power system
adequacy assessment using a post-processing method
- URL: http://arxiv.org/abs/2402.17039v2
- Date: Wed, 28 Feb 2024 12:23:56 GMT
- Title: Incorporating climate change effects into the European power system
adequacy assessment using a post-processing method
- Authors: In\`es Harang, Fabian Heymann, Laurens P. Stoop
- Abstract summary: The demand-supply balance of electricity systems is fundamentally linked to climate conditions.
The present study aims to model the effect of climate change on the European electricity system, specifically on its long-term reliability.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: The demand-supply balance of electricity systems is fundamentally linked to
climate conditions. In light of this, the present study aims to model the
effect of climate change on the European electricity system, specifically on
its long-term reliability. A resource adequate power system -- a system where
electricity supply covers demand -- is sensitive to generation capacity, demand
patterns, and the network structure and capacity. Climate change is foreseen to
affect each of these components.
In this analysis, we focused on two drivers of power system adequacy: the
impact of temperature variations on electricity demand, and of water inflows
changes on hydro generation. Using a post-processing approach, based on results
found in the literature, the inputs of a large-scale electricity market model
covering the European region were modified. The results show that climate
change may decrease total LOLE (Loss of Load Expectation) hours in Europe by
more than 50%, as demand will largely decrease because of a higher temperatures
during winter. We found that the climate change impact on demand tends to
decrease LOLE values, while the climate change effects on hydrological
conditions tend to increase LOLE values.
The study is built on a limited amount of open-source data and can flexibly
incorporate various sets of assumptions. Outcomes also show the current
difficulties to reliably model the effects of climate change on power system
adequacy. Overall, our presented method displays the relevance of climate
change effects in electricity network studies.
Related papers
- Robustness of AI-based weather forecasts in a changing climate [1.4779266690741741]
We show that current state-of-the-art machine learning models trained for weather forecasting in present-day climate produce skillful forecasts across different climate states.
Despite current limitations, our results suggest that data-driven machine learning models will provide powerful tools for climate science.
arXiv Detail & Related papers (2024-09-27T08:11:49Z) - Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region [62.09891513612252]
We focus on limited-area modeling and train our model specifically for localized region-level downstream tasks.
We consider the MENA region due to its unique climatic challenges, where accurate localized weather forecasting is crucial for managing water resources, agriculture and mitigating the impacts of extreme weather events.
Our study aims to validate the effectiveness of integrating parameter-efficient fine-tuning (PEFT) methodologies, specifically Low-Rank Adaptation (LoRA) and its variants, to enhance forecast accuracy, as well as training speed, computational resource utilization, and memory efficiency in weather and climate modeling for specific regions.
arXiv Detail & Related papers (2024-09-11T19:31:56Z) - Climate Change Impact on Agricultural Land Suitability: An Interpretable
Machine Learning-Based Eurasia Case Study [94.07737890568644]
As of 2021, approximately 828 million people worldwide are experiencing hunger and malnutrition.
Climate change significantly impacts agricultural land suitability, potentially leading to severe food shortages.
Our study focuses on Central Eurasia, a region burdened with economic and social challenges.
arXiv Detail & Related papers (2023-10-24T15:15:28Z) - Multi-scale Digital Twin: Developing a fast and physics-informed
surrogate model for groundwater contamination with uncertain climate models [53.44486283038738]
Climate change exacerbates the long-term soil management problem of groundwater contamination.
We develop a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Contaminated (PDENO)
In parallel, we develop a convolutional autoencoder combined with climate data to reduce the dimensionality of climatic region similarities across the United States.
arXiv Detail & Related papers (2022-11-20T06:46:35Z) - Causal Effect Estimation with Global Probabilistic Forecasting: A Case
Study of the Impact of Covid-19 Lockdowns on Energy Demand [2.126171264016785]
It is necessary to analyse the uncertainty of external intervention impacts on electricity demand.
This paper uses a deep learning approach to estimate the causal impact distribution of an intervention.
We consider the impact of Covid-19 lockdowns on energy usage as a case study.
arXiv Detail & Related papers (2022-09-19T09:39:29Z) - Reconstruction of Long-Term Historical Demand Data [0.9449650062296824]
We aim to better support the technology & policy development process for power systems by developing machine and deep learning 'back-forecasting' models.
By understanding the spatial and temporal variability of temperature over the US, the response of demand to natural variability and climate change-related effects on temperature can be separated.
arXiv Detail & Related papers (2022-09-10T15:27:10Z) - ClimateGAN: Raising Climate Change Awareness by Generating Images of
Floods [89.61670857155173]
We present our solution to simulate photo-realistic floods on authentic images.
We propose ClimateGAN, a model that leverages both simulated and real data for unsupervised domain adaptation and conditional image generation.
arXiv Detail & Related papers (2021-10-06T15:54:57Z) - Modelling the transition to a low-carbon energy supply [91.3755431537592]
A transition to a low-carbon electricity supply is crucial to limit the impacts of climate change.
Reducing carbon emissions could help prevent the world from reaching a tipping point, where runaway emissions are likely.
Runaway emissions could lead to extremes in weather conditions around the world.
arXiv Detail & Related papers (2021-09-25T12:37:05Z) - Dynamical Landscape and Multistability of a Climate Model [64.467612647225]
We find a third intermediate stable state in one of the two climate models we consider.
The combination of our approaches allows to identify how the negative feedback of ocean heat transport and entropy production drastically change the topography of Earth's climate.
arXiv Detail & Related papers (2020-10-20T15:31:38Z) - Augmented Convolutional LSTMs for Generation of High-Resolution Climate
Change Projections [1.7503398807380832]
We present auxiliary informed-temporal neural architecture for statistical downscaling.
Current study performs daily downscaling of precipitation variable from an ESM output at 1.15 degrees (115 km) to 0.25 degrees (25 km) over the world's most climatically diversified country, India.
arXiv Detail & Related papers (2020-09-23T17:52:09Z) - Modeling Climate Change Impact on Wind Power Resources Using Adaptive
Neuro-Fuzzy Inference System [1.293050392312921]
Middle and northern parts of the Caspian Sea are placed with the highest values of wind power.
Results of study indicated that the middle and northern parts of the Caspian Sea are placed with the highest values of wind power.
arXiv Detail & Related papers (2020-01-09T17:35:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.