From the 1 of 8 linked papers with an AI index.
8 papers
Optimal sizing of a hydrogen-based direct reduced iron-electric arc furnace system integrated with methanol synthesis toward zero-carbon steel production
Qiang Ji, Fashun Shi, Lin Cheng +4
The paper develops a fractional‑programming model to optimally size a hydrogen‑based direct reduced iron and electric arc furnace system integrated with methanol synthesis, evaluat…
A Process-Aware Demand Response Evaluation Framework for Hydrogen-Integrated Zero-Carbon Steel Plants Coupled with Methanol Production
Qiang Ji, Lin Cheng, Yue Zhou +4
High penetration of renewables (RES) and the retirement of thermal units aggravate flexibility scarcity in power systems. Hydrogen-based low-carbon steel production systems possess…
Quantum Learning and Estimation for Coordinated Operation between Distribution Networks and Energy Communities
Yingrui Zhuang, Lin Cheng, Yuji Cao +4
Price signals from distribution networks (DNs) guide energy communities (ECs) in adjusting their energy usage, enabling effective coordination for reliable power system operation.…
An Iterative Problem-Driven Scenario Reduction Framework for Stochastic Optimization with Conditional Value-at-Risk
Yingrui Zhuang, Lin Cheng, Ning Qi +2
Scenario reduction (SR) alleviates the computational complexity of scenario-based stochastic optimization with conditional value-at-risk (SBSO-CVaR) by identifying representative s…
Grid-Aware Real-Time Dispatch of Microgrid with Generalized Energy Storage: A Prediction-Free Online Optimization Approach
Kaidi Huang, Lin Cheng, Ning Qi +3
This paper proposes a novel prediction-free two-stage coordinated dispatch framework for the real-time dispatch of grid-connected microgrid with generalized energy storages (GES).…
A Weighted Predict-and-Optimize Framework for Power System Operation Considering Varying Impacts of Uncertainty
Yingrui Zhuang, Lin Cheng, Can Wan +3
Prediction deviations of different uncertainties have varying impacts on downstream decision-making. Improving the prediction accuracy of critical uncertainties with significant im…