Papers for
energy grid managers
Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.
Reinforcement learning improves pricing for hybrid vehicle stations
Reinforcement Learning-Based Pricing and Operation for Hybrid Electric and Hydrogen Vehicle Stations
Abstract: Electric vehicles (EVs) and hydrogen fuel cell vehi- cles (HFCVs) are promising technologies in the efforts to reduce carbon emissions. Challenges such as limited infrastructure, con- sumer dissatisfaction, and electrical grid strain inhibit progress in the adoption of these technologies. A zero emission vehicle (ZEV) station, capable of handling EVs and HFCVs, addresses these challenges by focusing on maximizing station profit and consumer utility, while minimizing grid impacts. The purpose of this research is to develop a realistic simulation of a ZEV station that is capable of handling real-time pricing decisions enabled by reinforcement learning (RL) and to compare the performance of the RL agent to static pricing methods. After training, the RL agent gains 10.83% greater station profit than the baseline manual parameter search. This work serves as a useful tool for ZEV station owners when handling complex pricing and purchasing decisions.
Job class aware liquid cooling improves ai data center heat control
Job Class Thermal Intent Aware Liquid Cooling Allocation for AI Data Centers
Abstract: GPU-dense AI data centers need to run on liquid cooling as air simply cannot shed the heat at these power densities. Yet the cooling loops themselves are blind to what workloads are about to run; they crank up flow only after a sensor catches a temperature climb, which can take 30 to 50 seconds. We built Job-Class Thermal Intent (JCTI) to close that window. The scheduler already knows a job is coming and what class it belongs to; JCTI feeds that information straight to the cooling controller so it can stage coolant before the heat shows up. We pulled the thermal signatures for each job class out of MLPerf GPU power traces and tuned arrival patterns against Alibaba cluster data. Over 120 paired Monte Carlo trials the numbers come out to 56.4% fewer thermal violations and 60.2% less cumulative overshoot than a straight PI loop. As AI data centers evolving towards gigawatt grid loads with highly fluctuating power swings, thermally-aware scheduling reduces sudden demand and improves load prediction in grid side. Cooling and scheduling have been running as two separate systems for years despite each one knowing something the other needs, JCTI wires them together.
Transfer learning improves solar power forecasts with less data
Transfer Learning with Conformalized Quantile Regression for Solar PV Forecasting Under Load-Shedding-Driven Data Scarcity
Abstract: Solar photovoltaic (PV) forecasting in regions affected by load shedding is challenging because reliable historical observations are scarce. This study proposes a transfer learning framework combined with Conformalized Quantile Regression (CQR) to improve PV power forecasting and provide reliable uncertainty estimates under severe data scarcity. A source-domain PV dataset from Alice Springs, Australia, is used to pretrain a temporal forecasting model, which is then adapted to simulated Bangladesh PV data representing different levels of historical availability. Experimental results show that transfer learning reduces RMSE by up to 23.7% when only one month of target-domain data is available and by 13.7% with three months of data. The proposed Transfer Learning plus CQR framework achieves 94.3% empirical coverage with three months of target data while producing prediction intervals that are 14% narrower than those obtained without transfer learning. These results demonstrate that combining transfer learning with conformal uncertainty quantification can improve both point forecasting accuracy and uncertainty reliability when target-domain PV data are severely limited.