most citedEnergy Management Based on Multi-Agent Deep Reinforcement Learning for A Multi-Energy Industrial Park

92 citations · 184 across the 5 of their papers we have counts for

collaborators

5 papers

eess.SY2022

Stochastic Gradient-based Fast Distributed Multi-Energy Management for an Industrial Park with Temporally-Coupled Constraints

Dafeng Zhu, Bo Yang, Chengbin Ma +4

Contemporary industrial parks are challenged by the growing concerns about high cost and low efficiency of energy supply. Moreover, in the case of uncertain supply/demand, how to m…

cs.CE202231 cited

Multi-level Coordinated Energy Management for Energy Hub in Hybrid Markets with Distributionally Robust Scheduling

Jiaxin Cao, Bo Yang, Shanying Zhu +2

Maintaining energy balance and economical operation is significant for multi-energy systems such as the energy hub. However, it is usually challenged by the frequently changing and…

cs.GT20221 cited

Bidirectional Pricing and Demand Response for Nanogrids with HVAC Systems

Jiaxin Cao, Bo Yang, Shanying Zhu +2

Owing to the fluctuant renewable generation and power demand, the energy surplus or deficit in each nanogrid is embodied differently across time. To stimulate local renewable energ…

cs.LG202260 cited

Asynchronous Decentralized Federated Learning for Collaborative Fault Diagnosis of PV Stations

Qi Liu, Bo Yang, Zhaojian Wang +4

Due to the different losses caused by various photovoltaic (PV) array faults, accurate diagnosis of fault types is becoming increasingly important. Compared with a single one, mult…

eess.SY202292 cited

Energy Management Based on Multi-Agent Deep Reinforcement Learning for A Multi-Energy Industrial Park

Dafeng Zhu, Bo Yang, Yuxiang Liu +3

Owing to large industrial energy consumption, industrial production has brought a huge burden to the grid in terms of renewable energy access and power supply. Due to the coupling…