A survey on physics informed reinforcement learning: Review and open problems
The inclusion of physical information in machine learning frameworks has revolutionized
many application areas. This involves enhancing the learning process by incorporating …
many application areas. This involves enhancing the learning process by incorporating …
Safe reinforcement learning for power system control: A review
The large-scale integration of intermittent renewable energy resources introduces increased
uncertainty and volatility to the supply side of power systems, thereby complicating system …
uncertainty and volatility to the supply side of power systems, thereby complicating system …
A mix-integer programming based deep reinforcement learning framework for optimal dispatch of energy storage system in distribution networks
The optimal dispatch of energy storage systems (ESSs) in distribution networks poses
significant challenges, primarily due to uncertainties of dynamic pricing, fluctuating demand …
significant challenges, primarily due to uncertainties of dynamic pricing, fluctuating demand …
Voltage control of distribution grid with district cooling systems based on scenario-classified reinforcement learning
Modern distribution grids are currently being challenged by frequent and sizable voltage
fluctuations, due mainly to the increasing deployment of renewable generation. Considering …
fluctuations, due mainly to the increasing deployment of renewable generation. Considering …
Intelligent control of district heating system based on RDPG
M Gong, Y Liu, J Sun, W Xu, W Li, C Yan… - Engineering Applications of …, 2024 - Elsevier
Given the continuous expansion of heating areas in recent years, the design of a precise
and dependable district heating system (DHS) has become increasingly crucial. Traditional …
and dependable district heating system (DHS) has become increasingly crucial. Traditional …
[HTML][HTML] Reinforcement learning for an enhanced energy flexibility controller incorporating predictive safety filter and adaptive policy updates
This paper presents a novel data-driven approach that leverages reinforcement learning to
enhance the efficiency and safety of existing energy flexibility controllers, addressing …
enhance the efficiency and safety of existing energy flexibility controllers, addressing …
A Constraint Enforcement Deep Reinforcement Learning Framework for Optimal Energy Storage Systems Dispatch
The optimal dispatch of energy storage systems (ESSs) presents formidable challenges due
to the uncertainty introduced by fluctuations in dynamic prices, demand consumption, and …
to the uncertainty introduced by fluctuations in dynamic prices, demand consumption, and …
Privacy-Preserving Bi-Level Optimization of Internet Data Centers for Electricity-Carbon Collaborative Demand Response
The escalating electrical demands of large-scale computational models in Internet data
centers (IDCs) coupled with their significant carbon footprint underscore the potential …
centers (IDCs) coupled with their significant carbon footprint underscore the potential …
Adaptive Tie-Line Power Smoothing With Renewable Generation Based on Risk-Aware Reinforcement Learning
The district cooling system (DCS) is a promising resource to smooth tie-line power
fluctuations in a grid-connected microgrid with high-penetration renewable generation …
fluctuations in a grid-connected microgrid with high-penetration renewable generation …
[HTML][HTML] Voltage-driven autonomous cooperative control of EHLs for load stabilization
B Qi, R Liu, D Zhang, Z Wang, Q Qi - … Journal of Electrical Power & Energy …, 2025 - Elsevier
Electric heating loads (EHLs) are considered as effective demand-side resources for supply–
demand balance. However, the effective control of EHLs becomes challenging due to their …
demand balance. However, the effective control of EHLs becomes challenging due to their …