The field of smart grid management and optimization is rapidly evolving, with a focus on developing innovative solutions to improve the efficiency, sustainability, and resilience of power systems. Recent research has explored the application of advanced control strategies, such as model predictive control and reinforcement learning, to optimize energy management and reduce grid stress. Additionally, there is a growing interest in integrating emerging technologies, such as battery storage and electric vehicles, into the grid to enhance flexibility and reduce emissions. Noteworthy papers in this area include: A Predictive Flexibility Aggregation Method for Low Voltage Distribution System Control, which proposes a novel approach to manage low-voltage distribution systems using predictive control and flexibility aggregation. Integrating Conductor Health into Dynamic Line Rating and Unit Commitment under Uncertainty, which develops a conductor health-aware unit commitment model to reduce the total cost and renewable curtailment in power systems. Bio-inspired Microgrid Management based on Brain's Sensorimotor Gating, which introduces a new paradigm for microgrid management inspired by the brain's sensorimotor gating mechanisms. Feature-driven reinforcement learning for photovoltaic in continuous intraday trading, which proposes a feature-driven reinforcement learning approach for photovoltaic intraday trading to improve revenues and reduce imbalance costs. Real-time Measurement-based Optimization for Distribution System Operation Considering Battery Voltage and Thermal Constraints, which develops a data-driven operational control scheme for battery storage in distribution systems to ensure secure operation and satisfaction of voltage and thermal constraints. Optimizing Energy Management of Smart Grid using Reinforcement Learning aided by Surrogate models built using Physics-informed Neural Networks, which addresses the sample efficiency problem in reinforcement learning by substituting costly smart grid simulators with surrogate models built using physics-informed neural networks. The Value of Patience in Online Grocery Shopping, which investigates the trade-off between individual convenience and societal costs in online grocery shopping and finds that modest increases in consumer patience can deliver substantial gains in traffic reduction and sustainability. Managing Charging Induced Grid Stress and Battery Degradation in Electric Taxi Fleets, which develops an EV fleet simulator to evaluate the impact of charging policies on grid stress, battery degradation, and profitability. Approximate Model Predictive Control for Microgrid Energy Management via Imitation Learning, which proposes an imitation learning-based framework to approximate mixed-integer economic model predictive control for microgrid energy management. Multi-layer Optimized Coordination of Smart Building Resources in Active Power Distribution Systems, which proposes a multi-actor coordination platform for the optimal utilization of smart building resources in active power distribution systems. Bilevel Analysis of Cost and Emissions Externalities from Data Center Load Shifting, which develops a bilevel optimization framework to analyze the impact of data center load shifting on power system operations and emissions.