Výsledky vyhledávání - "Chan, Ka Wing"

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Multi-Network Constrained Operational Optimization in Community Integrated Energy Systems: A Safe Reinforcement Learning Approach

Autor: Hu, Ze, Chan, Ka Wing, Zhu, Ziqing, Wei, Xiang, Bu, Siqi

The integrated community energy system (ICES) has emerged as a promising solution for enhancing the efficiency of the distribution system by effectively coordinating multiple energy sources. However, the operational optimization of ICES is hindered b

Externí odkaz: http://arxiv.org/abs/2402.05412

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Cooperative Dispatch of Microgrids Community Using Risk-Sensitive Reinforcement Learning with Monotonously Improved Performance

Autor: Zhu, Ziqing, Gao, Xiang, Bu, Siqi, Chan, Ka Wing, Zhou, Bin, Xia, Shiwei

The integration of individual microgrids (MGs) into Microgrid Clusters (MGCs) significantly improves the reliability and flexibility of energy supply, through resource sharing and ensuring backup during outages. The dispatch of MGCs is the key challe

Externí odkaz: http://arxiv.org/abs/2310.10997

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An Imitation Learning Based Algorithm Enabling Priori Knowledge Transfer in Modern Electricity Markets for Bayesian Nash Equilibrium Estimation

Autor: Zhu, Ziqing, Chan, Ka Wing, Bu, Siqi, Hu, Ze, Xia, Shiwei

The Nash Equilibrium (NE) estimation in bidding games of electricity markets is the key concern of both generation companies (GENCOs) for bidding strategy optimization and the Independent System Operator (ISO) for market surveillance. However, existi

Externí odkaz: http://arxiv.org/abs/2305.06924

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How to Use Reinforcement Learning to Facilitate Future Electricity Market Design? Part 2: Method and Applications

Autor: Zhu, Ziqing, Bu, Siqi, Chan, Ka Wing, Zhou, Bin, Xia, Shiwei

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 theory is further demonstrated by elaborating detailed methods of desig

Externí odkaz: http://arxiv.org/abs/2305.06921

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How to Use Reinforcement Learning to Facilitate Future Electricity Market Design? Part 1: A Paradigmatic Theory

Autor: Zhu, Ziqing, Bu, Siqi, Chan, Ka Wing, Zhou, Bin, Xia, Shiwei

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 of renewable generations, and to achieve power system operation securi

Externí odkaz: http://arxiv.org/abs/2305.02485

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Applications of Reinforcement Learning in Deregulated Power Market: A Comprehensive Review

Autor: Zhu, Ziqing, Hu, Ze, Chan, Ka Wing, Bu, Siqi, Zhou, Bin, Xia, Shiwei

The increasing penetration of renewable generations, along with the deregulation and marketization of power industry, promotes the transformation of power market operation paradigms. The optimal bidding strategy and dispatching methodology under thes

Externí odkaz: http://arxiv.org/abs/2205.08369

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