2 citations · 4 across the 6 of their papers we have counts for
6 papers
Quantum Reinforcement Learning-Based Two-Stage Unit Commitment Framework for Enhanced Power Systems Robustness
Xiang Wei, Ziqing Zhu, Linghua Zhu +5
Unit commitment (UC) optimizes the start-up and shutdown schedules of generating units to meet load demand while minimizing costs. However, the increasing integration of renewable…
Techno-Economic Modeling and Safe Operational Optimization of Multi-Network Constrained Integrated Community Energy Systems
Ze Hu, Ka Wing Chan, Ziqing Zhu +3
The integrated community energy system (ICES) has emerged as a promising solution for enhancing the efficiency of the distribution system by effectively coordinating multiple energ…
Cooperative Dispatch of Microgrids Community Using Risk-Sensitive Reinforcement Learning with Monotonously Improved Performance
Ziqing Zhu, Xiang Gao, Siqi Bu +3
The integration of individual microgrids (MGs) into Microgrid Clusters (MGCs) significantly improves the reliability and flexibility of energy supply, through resource sharing and…
How to Use Reinforcement Learning to Facilitate Future Electricity Market Design? Part 2: Method and Applications
Ziqing Zhu, Siqi Bu, Ka Wing Chan +2
This two-part paper develops a paradigmatic theory and detailed methods of the joint electricity market design using reinforcement-learning (RL)-based simulation. In Part 2, this t…
How to Use Reinforcement Learning to Facilitate Future Electricity Market Design? Part 1: A Paradigmatic Theory
Ziqing Zhu, Siqi Bu, Ka Wing Chan +2
In face of the pressing need of decarbonization in the power sector, the re-design of electricity market is necessary as a Marco-level approach to accommodate the high penetration…
Applications of Reinforcement Learning in Deregulated Power Market: A Comprehensive Review
Ziqing Zhu, Ze Hu, Ka Wing Chan +3
The increasing penetration of renewable generations, along with the deregulation and marketization of power industry, promotes the transformation of power market operation paradigm…