UC San Diego

The transition to a sustainable and resilient power grid is essential for addressing the environmental and socio-economic challenges posed by climate change. While the integration of renewable energy resources offers significant benefits, it also introduces complexities in grid management due to their non-dispatchable nature and the need for advanced energy storage solutions. This dissertation focuses on optimizing grid operations, managing renewable integration, and ensuring reliability under various conditions, presenting novel methodologies and insights into our future energy infrastructure. Grid decarbonization is a primary goal, driven by the need to reduce greenhouse gas emissions, mitigate climate change, and promote public health. It also fosters economic benefits such as green job creation and energy independence. However, the shift to a cleaner energy grid presents challenges, including the increased penetration of non-dispatchable resources like wind and solar, the necessity for advanced energy storage solutions, and the requirement for economic analyses to evaluate new technologies and policies. Additionally, upgrading the existing grid infrastructure to accommodate renewable energy sources is crucial to ensure reliability and resilience. Enhancing grid resilience is vital for mitigating the impacts of natural disasters, improving power system reliability and security, and ensuring continuity of supply to customers and critical loads. Achieving resilience involves modeling and incorporating risk into grid operations, preparing for natural disasters, and addressing the exponential complexity of optimization problems. This research introduces wildfire risk-aware planning and the strategic relocation of electric vehicles (EVs) for resilient grid operation as key components of a robust resilience framework. To further improve grid operations, this dissertation proposes an agile decision-support system that accelerates power grid optimization. Enhanced grid resilience is achieved by making the system more robust against disruptions, and improved decision-making is facilitated through faster and more accurate analyses. Real-time optimization becomes feasible, allowing for immediate adjustments to changing conditions, reducing costs, increasing grid flexibility, and improving the accuracy of predictions and planning. This leads to more effective utilization of renewable energy sources. This dissertation presents comprehensive strategies to enhance the resilience and sustainability of a decarbonized power grid. By leveraging advanced optimization techniques and incorporating dynamic models of natural gas and renewable energy interactions, it addresses the critical need for real-time optimization, improved decision-making, and effective renewable energy utilization, ultimately contributing to a more resilient and sustainable energy future.