Reinforcement Learning-Driven Microgrid Dispatch Under Extreme Weather Events: A Risk-Averse Decision Architecture for Coastal Cities

Abstract

Resilience in action through the growing effects of weather events that threaten the stability of coastal energy infrastructure systems and microgrids with risks not manageable by traditional dispatch techniques. Discuss Risk Averse Reinforcement Learning Decision Architecture (RARLDA), proposed with the intention of integrating risk responsiveness into traditional dispatch plans. Discuss the uniqueness of RARLDA over existing conventional reinforcement learning models through its adaptation from average performance measures into risk-aware decision-making with primary attention devoted to maximum wind speed, rainfall intensity, and temperature. Adopt a qualitative research methodology through the utilization of the “Top 100 Cities Weather Dataset” with the purpose of developing risk indices specific for coastal areas. The research also makes use of median imputation techniques for the elimination of any gaps in the dataset with additional focus on Z score normalization for maximum reliability of the dataset. Calculate the risk indices based on the use of the percentile-based thresholds, together with the use of the Conditional Value at Risk penalization, incorporating them into the RL reward function. The validity of the framework can be shown through the simulation outcome, thereby providing the measure of novelty and performance, where the value of the mean reward is calculated to be 9.84, the mean accuracy defined at 75.95%, and the mean risk index threshold derived at 0.070, thereby outdoing other techniques based upon stability and adaptability. Finally, the conclusion could be derived based upon the contribution to the field, thereby providing the insights into the integration of the risk associated with the climate change into the learning process itself, thereby providing the notion of a qualitatively verified and risk-sensitive model within the field of Resilient and Graceful Management of Energy Systems within the Coastal Cities.