Physical Informed-Inspired Deep Reinforcement Learning Based Bi-Level Programming for Microgrid Scheduling

no code implementations • 15 Oct 2024 • Yang Li, Jiankai Gao, Yuanzheng Li, Chen Chen, Sen Li, Mohammad Shahidehpour, Zhe Chen

To coordinate the interests of operator and users in a microgrid under complex and changeable operating conditions, this paper proposes a microgrid scheduling model considering the thermal flexibility of thermostatically controlled loads and demand response by leveraging physical informed-inspired deep reinforcement learning (DRL) based bi-level programming.

AutoML Computational Efficiency +3

Multi-Microgrid Collaborative Optimization Scheduling Using an Improved Multi-Agent Soft Actor-Critic Algorithm

no code implementations • 1 Apr 2023 • Jiankai Gao, Yang Li, Bin Wang, Haibo Wu

The implementation of a multi-microgrid (MMG) system with multiple renewable energy sources enables the facilitation of electricity trading.

AutoML Deep Reinforcement Learning +4

Optimal dispatch of low-carbon integrated energy system considering nuclear heating and carbon trading

no code implementations • 24 Sep 2022 • Yang Li, Fanjin Bu, Jiankai Gao, Guoqing Lia

The development of miniaturized nuclear power (NP) units and the improvement of the carbon trading market provide a new way to realize the low-carbon operation of integrated energy systems (IES).

Scheduling