Deep reinforcement learning from demonstrations to assist service restoration in islanded microgrids
Microgrids can be operated in island mode during utility grid outages to support service
restoration and improve system resilience. To schedule and dispatch distributed energy …
restoration and improve system resilience. To schedule and dispatch distributed energy …
Novel Data-Driven decentralized coordination model for electric vehicle aggregator and energy hub entities in multi-energy system using an improved multi-agent …
Energy hub (EH) is an independent entity that benefits to the efficiency, flexibility, and
reliability of integrated energy systems (IESs). On the other hand, the rapid emerging of …
reliability of integrated energy systems (IESs). On the other hand, the rapid emerging of …
Hybrid data-driven method for low-carbon economic energy management strategy in electricity-gas coupled energy systems based on transformer network and deep …
Because of their attractive economic and environmental benefits, integrated energy systems
(IESs), especially electricity-gas coupled energy systems (EGCESs), have received great …
(IESs), especially electricity-gas coupled energy systems (EGCESs), have received great …
Application of reinforcement learning in planning and operation of new power system towards carbon peaking and neutrality
To mitigate global climate change and ensure a sustainable energy future, China has
launched a new energy policy of achieving carbon peaking by 2030 and carbon neutrality …
launched a new energy policy of achieving carbon peaking by 2030 and carbon neutrality …
Physical-model-free intelligent energy management for a grid-connected hybrid wind-microturbine-PV-EV energy system via deep reinforcement learning approach
Renewable-based microgrid (MG) is recognized as an eco-friendly solution in the
development of renewable energy (RE). Moreover, the MG energy management with high …
development of renewable energy (RE). Moreover, the MG energy management with high …
A two-stage subsynchronous oscillation assessment method for DFIG-based wind farm grid-connected system
G Liu, J Liu, A Liu - Scientific Reports, 2024 - nature.com
In the power system, the wind farm based on Doubly-Fed Induction Generator (DFIG) may
lead to Subsynchronous Oscillation (SSO), which poses a challenge to the stability of the …
lead to Subsynchronous Oscillation (SSO), which poses a challenge to the stability of the …
A distributed dynamic inertia-droop control strategy based on multi-agent deep reinforcement learning for multiple paralleled VSGs
The virtual synchronous generator (VSG) control method for energy storage is a promising
way to improve the frequency stability of the power system with large-scale renewable …
way to improve the frequency stability of the power system with large-scale renewable …
Deep Entropy-Learning based virtual inertia control for VRFB regulation considering Phase-Locked loop dynamics
S Li, JT Makuza - Expert Systems with Applications, 2024 - Elsevier
The virtual inertia control (VIC) technique is often adopted as a prominent mechanism to
address the inertia deficiency of modern integrated power systems with high penetration of …
address the inertia deficiency of modern integrated power systems with high penetration of …
Deep reinforcement learning-based optimal scheduling of integrated energy systems for electricity, heat, and hydrogen storage
T Liang, X Zhang, J Tan, Y **g, L Liangnian - Electric Power Systems …, 2024 - Elsevier
The increasing load demands and the extensive usage of renewable energy in integrated
energy systems pose a challenge to the most efficient scheduling of integrated energy …
energy systems pose a challenge to the most efficient scheduling of integrated energy …
Deep reinforcement learning based approach for dynamic optimal power flow in active distribution network
X Liu, B Fan, J Tian - 2022 41st Chinese Control Conference …, 2022 - ieeexplore.ieee.org
The dynamics and randomness of the active distribution network (ADN) bring many
difficulties to optimal power flow (OPF). In this paper, we propose a deep reinforcement …
difficulties to optimal power flow (OPF). In this paper, we propose a deep reinforcement …