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Multi-Network Constrained Operational Optimization in Community Integrated Energy Systems: A Safe Reinforcement Learning Approach
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.
Cooperative Dispatch of Microgrids Community Using Risk-Sensitive Reinforcement Learning with Monotonously Improved Performance
This online algorithm enables MGs to spontaneously search for the Pareto Frontier considering multiple objectives and risk mitigation.
How to Use Reinforcement Learning to Facilitate Future Electricity Market Design? Part 1: A Paradigmatic Theory
In Part 1, the theory and framework of this novel market design philosophy are proposed.
An Imitation Learning Based Algorithm Enabling Priori Knowledge Transfer in Modern Electricity Markets for Bayesian Nash Equilibrium Estimation
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.
How to Use Reinforcement Learning to Facilitate Future Electricity Market Design? Part 2: Method and Applications
This two-part paper develops a paradigmatic theory and detailed methods of the joint electricity market design using reinforcement-learning (RL)-based simulation.
Applications of Reinforcement Learning in Deregulated Power Market: A Comprehensive Review
The increasing penetration of renewable generations, along with the deregulation and marketization of power industry, promotes the transformation of power market operation paradigms.
