Smart Microgrid: From intelligent monitoring to multi-energy
Acting as a multi-energy backup and intelligent microgrid, it is able to provide emergency power during grid outages, enhancing community resilience and ensuring reliable electricity for...
Xiong WU | Xi''an Jiaotong University, Xi''an | XJTU | Department of
This study presents a chance-constrained scheduling model based on probabilistic and robust optimisation to handle the uncertainty of renewable energy generation and loads in microgrids.
Advanced control strategy for AC microgrids: a hybrid ANN-based
In this paper, an improved voltage control strategy for microgrids (MG) is proposed, using an artificial neural network (ANN)-based adaptive proportional-integral (PI) controller combined
Deep reinforcement learning for optimal microgrid energy
This paper proposes a deep reinforcement learning (DRL)-based real-time optimal energy management method to assist the EMS for microgrids in making optimal scheduling decisions.
Xiong''an accelerates smart-city buildout
Some 20 meters below ground in Rongdong area of Xiong''an New Area, an integrated utility corridor houses nearly 10,000 devices spanning more than 20 categories, collecting real-time
Autonomous microgrid system using smart Python agents
This chapter presents smart Python agents for autonomous microgrid systems. The agents operate microgrids by integrating renewable energy resources and optimizing energy consumption while
Smart Microgrids: Developing the Intelligent Power Grid of Tomorrow
This paper presents an overview of our body of work on the application of smart control techniques for the control and management of microgrids (MGs). The main focus here is on the
"Autonomous Microgrid System" by Xavier Kuehn and Brian Xiong
This thesis presents smart python agents for microgrid systems to automate the operations and control of microgrid renewable resources in an effort to provide resilient solutions to
Optimizing electricity demand scheduling in microgrids using deep
Microgrids (MGs) are small-scale power generation and distribution systems that can effectively integrate renewable energy, electric loads, and energy storage systems (ESS). By using
Deep reinforcement learning for optimal microgrid energy
Robustness analysis demonstrates that quantum-enhanced exploration yields more resilient control strategies, suggesting that quantum-inspired reinforcement learning can significantly enhance