AI-driven: Decarbonization for Power Systems, Pt 1/4

This multiple session web-based tutorial: Will provide a comprehensive overview of strategies for power system decarbonization, focusing on three key approaches: electric vehicles, microgrids, and peer-to-peer energy trading. It will explore the associated challenges and demonstrate how advanced techniques, including deep reinforcement learning and big data analytics, can be leveraged to address these challenges effectively. Session one will discuss: Decarbonisation approach for power systems a. Power systems and decentralized flexibility b. Decarbonization framework c. Market design for low-carbon transitions The ongoing decarbonization of power systems is transforming their fundamental structure, driven by the rapid penetration of renewable energy sources (RESs) and the electrification of transport and heating sectors. Achieving ambitious carbon budgets requires accelerating the pace of deep decarbonization within deregulated markets. These transitions introduce significant techno-economic challenges, particularly in ensuring system reliability and operational efficiency. Enhanced flexibility is essential to address the variability and limited controllability of RESs. To complement centralized solutions, distributed energy resources (DERs) are increasingly deployed and operated in decentralized configurations. While these DERs contribute to system flexibility, they also amplify the complexity of managing distributed systems. Moreover, the vast amount of data exchanged in decentralized frameworks raises privacy concerns, necessitating secure and efficient data handling solutions. This tutorial will provide a comprehensive overview of strategies for power system decarbonization, focusing on three key approaches: electric vehicles, microgrids, and peer-to-peer energy trading. It will explore the associated challenges and demonstrate how advanced techniques, including deep reinforcement learning and big data analytics, can be leveraged to address these challenges effectively.