A recent study published in ‘IEEE Access’ has introduced a novel approach to enhancing power system resilience against winter storms, which can have devastating impacts on energy supply. Led by Brent G. Austgen from the Operations Research and Industrial Engineering Graduate Program at The University of Texas at Austin, the research presents a three-stage stochastic programming model that aims to guide risk-averse investments in critical infrastructure.
The model operates in three distinct phases. The first stage focuses on long-term investments in essential resources, such as generator winterization and mobile battery energy storage systems (MBESS). The second stage is concerned with the deployment of MBESS just before an anticipated storm, while the third stage emphasizes operational responses during the storm event itself. Notably, the model assumes that the severity of an imminent winter storm is known at the time of making deployment decisions, allowing for more precise and effective planning.
Austgen’s research incorporates a risk management strategy known as conditional value-at-risk (CVaR), which helps to minimize potential losses from unserved energy during these high-impact, low-frequency events. This approach is particularly relevant for energy providers and grid operators as they navigate the uncertainties associated with extreme weather events. “We demonstrate how the optimal investments are affected by parameters like cost and risk aversion,” Austgen explains, underscoring the importance of financial considerations in resilience planning.
The study applies its model to a Texas-focused case study, utilizing the ACTIVS 2000-bus synthetic grid and historical data from Winter Storm Uri. The findings reveal significant insights into how effectively using CVaR as a risk measure can mitigate adverse outcomes during winter storms. This information is crucial for energy companies looking to enhance their infrastructure and service reliability amid increasing climate-related challenges.
As the energy sector continues to grapple with the impacts of climate change, the findings from this research present both challenges and opportunities. Companies can leverage the insights from this model to make informed investment decisions that not only enhance resilience but also potentially lead to cost savings by reducing the likelihood of energy supply disruptions.
For more information on the research and its implications, you can visit the lead_author_affiliation.
